On-Demand Video Streaming Schemes Over Shared-WDM-PONs

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 23, NO. 9, SEPTEMBER 2013 1577

On-Demand Video Streaming Schemes OverShared-WDM-PONs

Sepideh Nikmanzar, Akbar Ghaffarpour Rahbar, Senior Member, IEEE, and Amin Ebrahimzadeh

Abstract—A critical challenge for video-on-demand (VoD)services is to provide an entertainment service with minimumplayback delay. A passive optical network (PON) that employsa high-speed optical fiber from an optical line terminal (OLT)to a number of optical network units (ONUs) can offer highbandwidth for multimedia applications (such as VoD services)in an access network. In this paper, we propose two novelvideo streaming techniques, called OLT broadcasting with ONUfast patching (BFP) and prediction-based OLT broadcasting andONU fast patching (PBFP). The BFP scheme utilizes the ONU fastpatching scheme at each ONU, and proposes a heuristic algorithmto find near optimum solutions of related optimization problemso that the worst-case playback delay (WPD) is minimized. ThePBFP adds a prediction level for video popularity to the BFPscheme and uses a seamless channel transition technique toseamlessly change the number of channels allocated to videos.We study the efficiency of the proposed schemes when theyare used in a shared wavelength division multiplexed passiveoptical network (Shared-WDM-PON) that adapts the broadcastnature of a GPON’s downstream wavelength to WDM-PONs.Our simulation results indicate that the proposed schemes canimprove both WPD and average playback delay performanceparameters.

Index Terms—Broadcasting, multicasting, passive opticalnetwork (PON), patching, video-on-demand (VoD).

Nomenclature

Di Duration of watching video i from 1 to Li in minutese The number of unassigned patching channelsGl The group of segments from S1 to Sl

i The counter of video titlesidij The interest degree of video i at ONU jj The counter of ONUsk The number of video segmentsLi The length of video i in minutesl The counter of patching channelsM Total number of video titles in video serverN Total number of ONUs

Manuscript received August 6, 2012; revised December 10, 2012; acceptedFebruary 3, 2013. Date of publication March 27, 2013; date of current versionAugust 30, 2013. This work was supported in part by the Research Institutefor ICT, Iran. This paper was recommended by Associate Editor W. Zeng.

S. Nikmanzar and A. Ebrahimzadeh are with the Sahand University ofTechnology, Sahand New Town, Tabriz, Iran (e-mail: [email protected];[email protected]).

A. G. Rahbar is with the Computer Networks Research Laboratory,Electrical Engineering Technologies Research Center, Sahand University ofTechnology, Tabriz, Iran (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TCSVT.2013.2254892

PCi The old patching channel i to patch video segments.PC′

i The new patching channel i to patch video segments.pij Probability of receiving request for video i at ONU

jp∗

ij The normalized values of popij

popij(t) Popularity of video i at ONU j at time tTp Transition time pointSi Old segment j in a video titleS′

i New segment j in a video title after transitionWi Size of patching window of video iXj Total number of patching channels allocated to ONU

jxij The number of patching channels allocated to video

i at the ONU jx′

ij The number of new patching channels allocated tovideo i at ONU j for channel transition

x∗ij The nearest integer value obtained by rounding of xij

Y Total number of broadcasting channels allocated tothe OLT

yi The number of broadcasting channels from OLT toONU allocated to video i.

α Prediction factor to reduce the speed of change ofpopularity of each video

� The Zipf distribution parameter� Total request arrivals rate (per minute)�ij Request arrival rate for video i at ONU jλj The Lagrange multiplier vectorϕi The threshold of a user’s interest to a video iωi Wavelength channel i

I. Introduction

V IDEO-ON-DEMAND (VoD) enables clients to browse,select, and watch a video title stored in a set of video

servers at any time with enhanced interactivity [1]. NetworkI/O bandwidth and playback delay are two main technicalchallenges that have been annoying video content providersin development of VoD systems [2]. These limitations make aVoD service as an inefficient and expensive service.

A passive optical network (PON) can meet the high net-work bandwidth demand for transmission of on-demand videostreams in access networks [3]. Wavelength division multi-plexing PON (WDM-PON) and time division multiplexingPON (TDM-PON) are two variants of PON. Gigabit-capablePON (GPON) according to ITU-T G.984 [4] is a widespreadimplementation of the TDM-PON, in which time slots are

1051-8215 c© 2013 IEEE

1578 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 23, NO. 9, SEPTEMBER 2013

allocated to each ONU. The WDM-PON may dedicate aseparate wavelength channel to each ONU.

Shared wavelength division multiplexing passive optical net-work (Shared-WDM-PON) proposed in [5] adapts the broad-cast nature of a GPON’s downstream wavelength to WDM-PONs. A Shared-WDM-PON is composed of an optical lineterminal (OLT), a feeder fiber, a remote node, and a number ofoptical network units (ONUs). The most promising feature ofa Shared-WDM-PON is to use a shared GPON’s downstreamwavelength in a generic WDM-PON for multicasting (orbroadcasting) the most popular videos [5].

To reduce the bandwidth requirement of VoD services,different multicasting techniques have been proposed to deliverone video stream to multiple clients simultaneously, thus effi-ciently utilizing network bandwidth. Batching/delayed multi-cast [6]–[11], patching/controlled multicast/stream tapping [2],[12]–[19], stream merging [20]–[22] are typical approachesthat use multicast transmission when a client request arrives.In batching, clients’ requests arriving at a time interval arebatched together and served via a single multicast stream. Apatching scheme introduces patching unicast/multicast streamsfor delivering the missing portion of a video to later requests.Stream merging takes the advantages of patching idea formerging all requests for the same video into larger groups.

Patching or dynamic multicasting [12] approaches exploitlocal buffer at each client side to allow a new client to jointhe latest ongoing multicast stream. This is done by cachingsubsequent data from an ongoing multicast, whereas playingthe leading portion of video from the beginning. When theplayback of the leading portion is ended, a client continues toplayback the remainder of the video already buffered. Theidea of controlled client-initiated-with-pre-fetching (CIWP),also known as threshold-based multicasting [16], is similar tothe patching scheme. However, the controlled CIWP schemeapplies an optimal threshold to adjust the frequency at whicha complete stream of a video starts. Fragmented patching [17]provides mobility for clients in broadband wireless environ-ments. Due to the extreme overhead of unicasting in mobileenvironments, patching streams are broken into segments andare sent via broadcasting. Hierarchical patching [15] multicaststhe video in the form of patches. The purpose of this scheme isto find a set of patches that covers the whole video for all re-quests. The slotted patching [18] divides a video into time slotswith uniform length. If there is at least one request in a timeslot, the slotted patching dispatches a patching channel in thattime slot. Fast patching (FP) [19] implements modified seg-ment allocation of the fast broadcasting at patching channels.

A variety of broadcasting protocols [23] have been pro-posed to periodically broadcast video segments through logicalchannels. The clients must be able to catch video data fromone or more channels [24]. Fast broadcasting [25], skyscraperbroadcasting [26], pyramid broadcasting [27], pagoda broad-casting [28], and harmonic broadcasting [29], [30], greedyequal-bandwidth broadcasting [31], and generalized Fibonaccibroadcasting [32], [33] are different broadcasting protocolsthat have been designed for reducing user waiting time athigh request arrival rates. These protocols differ in partitioningvideos into segments and the order of sending segments over

broadcasting channels [34]. Sharing channels among users andincreasing the utilization of bandwidth are two advantages ofbroadcasting methods. The fast broadcasting (FB) [25] schemehas been proposed to reduce the waiting time experienced byclients. The FB partitions a video into 2k − 1 segments ofequal sizes and transmit them on k channels. The seamlessFB [35] scheme has been suggested to seamlessly transit thenumber of channels assigned by FB to a video such that laterclients do not feel any disruption. Yu et al. [36] presented thesmooth fast broadcasting scheme for VBR-encoded videos.The reverse FB [37] scheme is a modified version of FB thatimproves the FB scheme on buffer requirement.

In order to minimize playback delay, the coordinated OLTbroadcast scheduling and ONU patching scheme has beenproposed in [38], called the CBP mechanism in this paperfrom now on. According to the CBP, the OLT broadcasts videoobjects at regular time intervals and each ONU broadcasts theinitial segments of a video according to the patch scheduling(PS) algorithm to minimize the start-up delay experienced byend users. The CBP is based on patching idea. Broadcastingstream from OLT to ONU is equivalent to regular multicaststream in patching. Broadcasting stream transmitted from anONU to its clients is equivalent to patching stream. The OLTscheduler solves an optimization problem for finding the opti-mal number of OLT broadcasting channels and ONU patchingchannels in such a way that the worst-case playback delay(WPD) is minimized [38]. The CBP scheme has also beenapplied to integrated fiber-wireless (FiWi) access networks[39]. A two-phase heuristic algorithm has been proposed toachieve minimum WPD over FiWi structure by consideringthat the storage at ONU has a limited space [39].

TV content recommendation techniques have been imple-mented in personal program guide (PPG) for digital TVsystems [40]. A TV content recommendation technique basedon fuzzy logic has been proposed in [41]. It has developeda multiagent recommendation system to recommend TV pro-grams according to users’ interests.

As stated, multicasting schemes exploit network bandwidthresources. A drawback of these approaches is that theyconsume high bandwidth when the requested rate is high[24], [42]. Although broadcasting schemes, e.g., [25]–[34],can present an efficient way to utilizes network bandwidth,they increase the startup delay [43]. Proposing a zero-delaytransmitting scheme can improve the system performance,but at the expense of high server bandwidth requirement.There is a tradeoff between minimizing playback delay andbandwidth consumption in video delivery techniques [20], buthigh playback delay may increase the number of clients wholeave the system without being served because of exceedingtheir waiting time from their waiting tolerance. Therefore, thesystem throughout will be decreased. These problems motivateus to design a more efficient VoD delivery scheme in termsof playback delay, called the OLT broadcasting and ONUfast patching (BFP) scheme and the prediction-based OLTbroadcasting with ONU fast patching (PBFP) scheme.

The objective of this paper is to present the OLT broad-casting and ONU fast patching (BFP) mechanism, whichutilizes ONU fast patching scheme for providing patching

NIKMANZAR et al.: ON-DEMAND VIDEO STREAMING SCHEMES OVER SHARED-WDM-PONS 1579

operation at the ONU side. Our proposed scheme combinesthe broadcasting technique with the idea of patching at eachONU. Broadcasting a popular video reduces the playbackdelay under the same bandwidth requirements. The OLT usesa simplest form of broadcasting to deliver video to all ONUsin this paper. We develop an optimization problem regardingthe optimal allocation of available logical channels at theOLT and ONUs by achieving minimum WPD. A heuristicalgorithm for generating near optimum solutions to solve theoptimization problem is proposed. Moreover, we develop thePBFP video delivering scheme that adds enhancement featurefor predicting popularity of each video to the BFP scheme. Theimportant aspect of the proposed scheme is that the bandwidthallocated to a video relies upon the video popularity. TheBFP scheme statically arranges the assigned channels for eachvideo. However, PBFP must dynamically increase or decreasethe number of channels assigned to a video in a seamlessmanner. The PBFP utilizes practical channel transition (PCT)[44] scheme for adjusting patching channels at the ONU side.The PBFP scheme introduces less playback delay becauseit predicts the popular videos favored by more clients, afterthat it allocates more channels for more popular videos. Whatmakes our proposed scheme unique is collecting data aboutusers’ preferences to foretell the popularity of video titlesat each ONU and seamlessly transit channels on-the-fly inShared-WDM-PON network structure. Notice that the ONUupdates statistical data every time. The OLT scheduler usesthis statistical data for allocating proper broadcasting/patchingchannels.

The principal contributions of this paper are: 1) introducingBFP based on the ONU fast patching technique, developingan optimization problem in conjunction with the patchingtechnique applied at ONU, and providing a heuristic algorithmto solve the optimization problem to achieve the near optimalWPD; and 2) proposing the PBFP approach as an on-demandvideo streaming scheme for shared-WDM-PON network thatcan alleviate clients’ waiting time by dynamically changingthe assigned channels based on the video popularity.

The remainder of this paper is organized as follows.Section II presents the network model, assumptions, and defi-nitions. Section III details the BFP approach. In Section IV, wedescribe the PBFP scheme. Section V provides performanceevaluation. This paper is concluded in Section VI.

We use the symbols and notations in Table I for describingthe parameters and workload characteristics of the system.

II. Network Model, Assumptions, and Definitions

A shared-WDM-PON architecture [5] that integrates aGPON downstream wavelength channel with a generic WDM-PON is considered to provide broadcast video sharing. Asdepicted in Fig. 1, the Shared-WDM-PON architecture con-sists of N ONUs connected to an OLT in a tree topology.The OLT is located at the central office where a numberof video servers or proxy video servers are connected to itvia a gigabit network. Assume that a number of EthernetLANs (including 1 Gbit/s and 10 Gbit/s LANs) are attachedto each ONU as a residential network. A set-top box (STB)

at the client side is equipped with a storage disk with enoughspace to accommodate some portions of a playing video. As aresult, the Shared-WDM-PON architecture provides sufficientbandwidth for doing a patching mechanism.

The first wavelength ω0 is the GPON downstream wave-length dedicated for broadcasting a popular video to allONUs. The wavelengths ω1, . . . , ωN are used for downstreamtransmission allocated to ONU1 to ONUN , respectively, inWDM PON. The wavelengths ωN+1, . . . , ω2N are dedicated toeach ONU for upstream transmission on a single feeder fiber.The OLT is equipped with N + 1 fixed transmitter for sendingthe downstream traffic and N fixed receivers to accommodateupstream traffic from each ONU. Each ONUi has a fixedreceiver and a fixed transmitter tuned to upstream wavelengthωi and downstream wavelength ω2i, respectively. Moreover,each ONU is equipped with a fixed receiver for receivingtraffic on wavelength ω0.

The remote node takes the advantage of loop-back configu-ration to route wavelength ω0 to all ONUs [5]. All multiplexedsignals with wavelengths ω0, . . . , ωN are entered into an[N+1]×[N+1] AWG and demultiplexed into the output portof the AWG. Moreover, a 1:N splitter is placed to splitwavelength ω0 into N copies. Each copied wavelength isfolded back into the input port AWG to deliver the broadcasttraffic to N ONUs.

The network topology is considered uniform where ONUsare located at equal distances from the OLT. Popular videosare broadcast on the GPON downstream channel by point-to-multipoint connectivity, whereas less popular videos areunicast along the dedicated WDM wavelengths to each ONUby point-to-point connections. In this paper, our main focus ison the scheduling of popular videos.

We assume that there is a storage with sufficient space atevery ONU to store the initial portion of each video. The ONUbuffers the first segments of a popular video title in its storageand uses them for patching the initial portion of the requestedvideo according to the ONU fast patching algorithm for itslocal clients.

In the following study, a logical channel is referred to asone unit of VoD server bandwidth used for transmission of adistinct stream of video data [45]. The bandwidth of GPONwavelength ω0 is subdivided into multiple logical channels.Subdividing available bandwidth of a wavelength channel intomultiple logical channels can be realized by time divisionmultiplexing (TDM) [34]. Assume that there are Y logicalchannels from the OLT to ONUs for broadcasting videos,each referred to as a broadcasting channel. In addition, Xj

additional channels (referred to as patching channels) arerequired at ONU j for patching the videos. The OLT schedulerpartitions Y broadcasting channels among all popular videos.Let the number of broadcasting channels allocated to videoi be yi. Moreover, Xj available channels at ONUj aredivided among M popular videos. Let xij denote the numberof patching channels allocated to video i at ONUj where∑M

i=1 xij = Xj .We assume that each video title is stored in a video server

with constant bit rate so that any video segment can betransmitted within a fixed time slot. If a client wishes to


Fig. 1. Shared-WDM-PON architecture.

view this video, he/she must wait for the first segment onthe first channel, and downloads all ongoing segments. Thus,the worst-case playback delay time equals the length of eachsegment.

When a client requests watching a video title, his STB issuesa request message to the ONU. The ONU uses the bufferedvideo data from its local storage to retrieve the missinginitial portion of the requested video. The ONU forwards therequest message to the OLT. Based on which, the OLT beginstransmission of a broadcast stream to all ONUs along withthe GPON wavelength channel or continues the transmissionof ongoing broadcast streams.

When a client decides to turn off a presented video, his/herSTB must send a leave message to the relevant ONU. Then, theONU stops forwarding the patching and broadcasting streamsimmediately for the client when it receives the leave request.These request and leave messages can be implemented withIGMP join message and IGMP leave message [46].

III. OLT Broadcasting and ONU Fast Patching

The OLT broadcasting and ONU fast patching (BFP)scheme is proposed in this section. We use the basic ideaof FB protocol [25] for patching the initial portion of thepopular videos at ONU and call it ONU fast patching. Anew heuristic algorithm, the OLT broadcasting and ONU fastpatching (BFP), is proposed for determining the number ofpatching channels at each ONU to optimize average WPDover all ONUs.

A. OLT Broadcasting Strategy

Assume that the length of video i is Li. The OLT repeatedlybroadcasts all video segments on the first channel y1 at time0. Video will be rebroadcast on channels y2 to yi in everyWi time units. Parameter Wi, called the patching window of

video i, is a fixed time interval between consecutive broadcaststreams of a video, which equals Li/yi. An ONU multicaststhe broadcast streams transmitted by the OLT to its clientsthat have requested video i. For example, consider that videoi is divided into 45 equal-sized segments. As shown inFig. 2, the OLT has dedicated three broadcasting channels fortransmission of this video. The OLT periodically broadcaststhe video on broadcasting channels 1 to 4 in every W = 15 timeunits. Patching of initial segments of video i is implementedusing the ONU fast patching technique by each ONU.

B. ONU Fast Patching

In the ONU fast patching scheme, a video is divided intoequal size segments. The number and the length of each videosegment are determined based on the number of patchingchannels and broadcasting channels allocated to a video byan ONU and the OLT. The following steps are involved inscheduling a video in an ONU according to the ONU fastpatching scheme.

1) Suppose that the number of allocated patching channelsto video i at ONU j is xij > 1. A video is dividedinto k segments, denoted by S1, S2 . . . Sk, where k =yi × (2xij − 1).

2) Patching channel PCi periodically broadcasts/multicaststhe segments of 2i−1 to 2i−1 in sequence, for i = 1, . . . ,xij . An ONU scheduler greedily transmits 2i−1 numberof video segments on patching channel i periodicallyand repeatedly.

3) If xij is equal to 1 then k = 2 × yi. The ONUdoes not transmit segment S1 on one allocated patchingchannel. The OLT just needs to broadcast this video viabroadcasting channels at the patching window size ofWi/2.

Fig. 2 shows an example of how ONU transmits videosegments according to the ONU fast patching when yi = 3


Fig. 2. Transmitting segments for one video using four patching channels at the ONU under the ONU fast patching scheme.

and uij = 4. The number of segments are 3×(24 − 1) = 45.The first patching channel PC1 transmits only the first segmentS1 repeatedly in such a way that all requests receiving at anytime slots can receive the first segment of the video object atthe beginning of the next time slot. Other patching channelstransmit the remaining segments according to the patternspecified in the ONU fast patching scheme in a cycle order.The PC2 transmits the next segments S2 and S3 periodically.Segments S4, S5, . . . , S7 will be transmitted on PC3. PC4 willtransmit the segments with index 8–15.

Let t = 8.5 be the time instant that a client issues a requestfor a video. As shown in Fig. 2, the client starts loading S10 upto the latest segment of the video form broadcasting channel1. At the same time, the client first downloads segment S1

from PC1 at time 9, segments S2 and S3 from PC2, segmentsS4 to S7 from PC3, and S8 to S9 from PC4. At any time, eachrequest can receive missing initial segments on any patchingchannels 1–4.

The ONU fast patching scheme assumes that clients canreceive data from all patching channels in addition to onebroadcasting channel used for broadcasting segments of avideo. If the client side bandwidth is limited, the modifiedversion of the FB scheme [47] can be used. In the modified FB,the client bandwidth never exceeds three or four logical chan-nels. Assume that an STB can receive video segments from thefirst m of xij channels. The STB first downloads data from thefirst m channels. When the STB drops the first m channels, itstarts to download data from the remaining xij − m channels.

C. OLT Broadcasting and ONU Fast Patching Algorithm

In this section, we formulate the relevant optimizationproblem for the ONU fast patching where a limited numberof broadcasting channels and patching channels are allocatedto all popular videos in the system in order to minimize WPDover all ONUs. We propose a heuristic algorithm to solvethe optimization problem that determines the near optimal

number of patching channels using the Karush–Kuhn–Tuckerconditions [48].

When video i is broadcast on yi broadcasting channels bythe OLT and patched using the ONU fast patching algorithmon xij patching channels at ONUj , the size of each segment isgiven by Li

yi×(2xij −1) . Since clients have to wait for the beginningof the first segment after issuing their requests to the ONU,playback delay time varies from zero to the length of the firstsegment. Thus, the WPD time for video i using the ONUfast patching is given by Li

yi×(2xij −1) for all 1 ≤ i ≤ M and1 ≤ j ≤ N. The probability of receiving a user request forvideo i at ONU j is pij where

∑Mi=1 pij = 1 for all 1 ≤ j ≤

N. Let pij be the probability for requesting video i at ONUj, where

∑Mi=1 pij = 1. It is assumed that the OLT scheduler

knows the value of pij for solving the optimization problem.The purpose of proposing an optimization problem is howto partition total number of Y broadcasting channels and Xj

patching channels into all popular videos to minimize averageWPD over all ONUs. The weighted sum of WDP for allbroadcast videos can be formulated as

∑Mi=1

pijLi/yi

2xij −1 .The optimization problem for finding broadcasting channel

allocation vector (y1, . . . , yM) and xij to minimize the averageweighted sum of WDP over all ONUs can be formulated asfollows:

minyj,xij

1

N

N∑j=1

M∑i=1

pijLi/yi

2xij − 1(1)

subject toM∑i=1

yi = Y

M∑i=1

xij = Xj

yi ∈ {1, 2, . . . , Y}, 1 ≤ i ≤ M

xij ∈ {0, 1, . . . , Xj}, 1 ≤ i ≤ M, 1 ≤ j ≤ N.


This optimization problem is an integer nonliner opti-mization problem. Dynamic programming can be applied forsolving the above nonlinear optimization problem. However,our optimization problem cannot be solved in polynomial timeby dynamic programming.

We propose a heuristic algorithm for determining the num-ber of patching channels at each ONU considering averageWPD over all ONUs. The heuristic algorithm solves the opti-mization problem using the Lagrangian relaxation technique.From (1), let

fj(x1j, ..., xMj) =M∑i=1

pijLi/yi

2xij − 1.

We have the following function:

Fj(x1j, ..., xMj, λj) =M∑i=1

pijLi/yi

2xij − 1+ λj(

M∑i=1

xij − Xj)

where λj, j = 1, ..., N is the Lagrange multiplier vector. For i= 1, . . . , M, we have

∂Fj

∂xij

=−2xij ln 2pijLi/yi

(2xij − 1)2+ λj = 0

which yields

xij = log

⎛⎝−(2 − ln 2pijLi/yi

λj) ±

√(2 − ln 2pijLi/yi

λj)2 − 4

2

⎞⎠ .

(2)In (2) and all the equations from now on, logarithmin base 2 isshown with log. Substituting (2) into x1j + x2j + ... + xMj = Xj ,we have

log

⎛⎝

M∏i=1

−(2 − ln 2pijLi/yi

λj) ±

√(2 − ln 2pijLi/yi

λj)2 − 4

2

⎞⎠ = Xj.

Let

g(λi)= log

⎛⎝

M∏i=1

−(2− ln 2pijLi/yi

λj)±

√(2− ln 2pijLi/yi

λj)2−4

2

⎞⎠ −Xj.

(3)For obtaining optimal values of x1j , x2j, . . . , xMj , the value

of λj must be computed from (3). The bisection method[49] for finding the roots of the nonlinear equation in (3) isapplied. We set yi = Y/M. The estimated value of λj usingby the bisection method is substituted into (2), and value ofx1j, x2j, . . . , xMj are calculated.

In summary, we propose the BFP algorithm to solve thenonlinear programming problem. Under the BFP scheme, theOLT scheduler executes the algorithm displayed in Fig. 3 tocompute near optimal xij . Fig. 3 indicates the pseudocode ofthe BFP algorithm.

Initially, all elements in yi are set to Y/M. To compute λj ,we use the bisection method for finding the root of (3) at line6. At line 7, the BFP algorithm calculates xij by substitutingλj into (4), for i = 1, . . . , M, j = 1, . . . , N. Let x∗

ij denote theinteger value obtained by rounding xij to its nearest integervalue. Line 8 rounds xij into an integer value x∗

ij . Line 12

Fig. 3. Pseudocode of the BFP algorithm.

computes the number of unassigned patching channels e =Xj−∑Mi=1 x∗

ij .When we have e > 0, the algorithm sorts x∗

ij in descendingorder of xij − x∗

ij . The values of x∗1j, . . . , x

∗ej are increased

by 1 unit considering the number of unassigned patchingchannels e (lines 15–17). If e < 0, then the number of assignedpatching channels is larger than Xj . Therefore, we sort x∗

ij in anascending order of xij −x∗

ij at line 19. The additional assignedpatching channels will be applied to decrease x∗

1j, . . . , x∗ej each

by 1 (lines 20–22).We compute the time complexity of the BFP algorithm

presented in Fig. 3. Lines 1–3 in the BFP algorithm need O(M)complexity. The time complexity of the bisection method tofind the root of (3), is O(log2( |b−a|

|pn−p| ), where one root of (3)is in the interval (a, b), and |pn – p|denotes the differencebetween the computed solution and real root (i.e., convergencetolerance). Since the values of a, b, and |pn – p| are knownbefore running the algorithm, the time complexity of thebisection method is bound by a value that is independent of thesize of the problem’s input. Therefore, the bisection methodhas a constant time complexity, O(1). In this case, lines from4 to 10 have O(M×N) time complexity. If we use a sortingalgorithm with time complexity O(M×log2M), lines from 11to 24 need O(M × N × log2M) complexity. Thus, the totalcomplexity of BFP algorithm is O(M×N×log2M).

IV. Prediction-Based OLT Broadcasting

and ONU Fast Patching

This section details our PBFP scheme. As described inSection I, the purpose of this scheme is to reduce theaverage/worst-case playback delay experienced by clients for


VoD services over Shared-WDM-PON. The PBFP allows anONU to track the users’ preferences to predict the popularityof each video with low computational requirement. The OLTscheduler uses the predicted information to determine the morerealistic number of patching channels and broadcasting chan-nels. We explain the ONU predictor functions in Section IV-A. The OLT scheduler function of the PBFP scheme shallbe presented in Section IV-B. Finally, the channel transitionstrategy will be explained in Section IV-C.

A. ONU Predictor

As mentioned before, the OLT broadcasts the entire body ofvideo object i using broadcasting channels at fixed time inter-vals Wi. An ONU buffers the first segments of video (size ofpatching window Wi) from the OLT broadcast stream. Patchinginitial segment of a single video on an ONU is similar to ONUfast patching algorithm, as described in Section III-B. The PCTscheme is used to seamlessly transit the number of patchingchannels predicted by video popularity such that the clientscurrently viewing this video will not experience any disruption.We will explain the process of PCT in Section IV-C.

Each ONU has an agent called ONU predictor for tracingusers’ preferences. The ONU predictor implements a predic-tion algorithm to predict the popularity of each video. TheONU predictor keeps an array of M elements called predictionarray. The ith element of the prediction array represents thepopularity of the ith video predicted at prior time.

Now, we consider an ONU and describe the functions of theONU predictor. When a client terminates watching a video, hisSTB issues a leave message to its relevant ONU and stampsthe duration of watching video i (denoted by Di). Based on thevalue of parameter Di, the ONU predictor at ONU j calculatesthe popularity of video i (i.e., popij) at time t, where popij hasa value between 0 and 1, where popij ∈ [0, 1]. Parameter popij

represents how much video i is favored at ONU j. Equation(4) estimates popij at current time t using the previous valueof popij at time t – 1

popij(t) = (1 − α)popij(t − 1) + α(Max(Di − ϕi, 0)

Li

) (4)

where α is the prediction factor to reduce the speed of changeof popij , and ϕi is the threshold of users’ interest to video i.If Di is larger than threshold ϕi, then video i is considereda favorable video by the viewers attached to ONU j. A largepopij demonstrates that the video is more popular for ONUj clients, and a smaller popij indicates that the video is notpopular at that ONU. For each leave request for video i,the ONU predictor updates the prediction array using (4).This prediction algorithm does not need to keep statisticsinformation about the users’ viewing history.

The pseudocode of the PBFP algorithm in ONUj is depictedin Fig. 4. The prediction factor α is initialized to 0.1. Sincethe ONU predictor does not have any knowledge about thepopularity of videos, popij is set to 0.5. The threshold of users’interest to video ϕi is Li/6. For example, the threshold of users’interest for a video with 120 min is ϕ = 20 min. The loop atline 6 is repeated for each leave message LM received for

Fig. 4. Pseudocode of the PBFP algorithm in ONUj .

video i. The ONU predictor obtains the duration of watchingvideo Di from LM and updates popij according to (4).

An ONU predictor periodically predicts the popularity ofeach video and sends the prediction array to the OLT schedulerfor determining the number of channels. The ONU transmitsthe prediction vector to the OLT in a report message. Whenthe OLT receives the report message, it will use the predictionarrays received from all ONUs for scheduling. In this way,an ONU predictor provides more realistic information of thepopularity video for the OLT scheduler.

B. OLT Scheduler

Suppose that the OLT has totally Y broadcasting channels tosupport M popular videos and ONUj has totally Xj patchingchannels for patching functions. According to the ONU fastpatching algorithm, the WPD for video i is Li

yi×(2xij −1) . Ourgoal is to minimize the weighted sum of WDP for all broad-cast videos, i.e.

∑Mi=1

pijLi/yi

2xij −1 . The OLT scheduler solves thefollowing optimization problem for minimizing the averageweight of WPD for all broadcast videos over all ONUs:

minyj,xij

1

N

N∑j=1

M∑i=1

p∗ijLi/yi

2xij − 1(5)

subject toM∑i=1

yi = Y

M∑i=1

xij = Xj

yj ∈ {1, 2, . . . , Y}xij ∈ {0, 1, . . . , Xj}

where yi is the number of broadcasting channels assigned tovideo i where

∑Mi=1 yi = Y .

The OLT scheduler uses the values of the prediction arraypopij reported by each ONU. The values of popij must benormalized into a number between 0 and 1 in such a way that∑M

i=1 popij = 1. Let us express the normalized values of popij

in terms of p∗ij in (5). The predicted value of p∗

ij helps the OLTscheduler to determine the number of patching channels andbroadcasting channels based on realistic knowledge of videopopularity at each ONU. We consider that the OLT scheduler


Fig. 5. Procedure of the PBFP scheme.

solves the above optimization problem using the heuristicalgorithm proposed in Section III-C of with a polynomial timecomplexity.

Fig. 5 illustrates the functional procedure of the PBFPscheme. The ONU predictor monitors the leave massages, cal-culates prediction array, and sends the result to the OLT sched-uler. Based on the perdition array received from ONUs, theOLT scheduler solves the optimization problem to determinethe number of patching channels and broadcasting channels.The OLT scheduler calculates the OLT’s broadcasting scheduleyi and ONU’s patching schedules xij . The OLT informs xij toall ONU schedulers. The PBFP scheme is not complex to trackthe users’ preferences at each ONU. It just needs to updatethe elements of the prediction array.

C. Channel Transition Scheme

The OLT scheduler calculates the number of patchingchannels and sends the results to all ONU schedulers. ThePBFP scheme uses a seamless channel transition strategy forincreasing or decreasing the number of allocated patchingchannels. Old clients receiving segments before channel transi-tion will not experience disruption. Seamless fast broadcasting[35] uses data padding strategy to enlarge the original video.This strategy causes some overhead such as startup delay. ThePCT [44] does not need data padding and is a simple and fastmethod to transit channels.

After receiving the new value of xij , the number of patchingchannels needs to be seamlessly changed from old value of xij

to new value of xij . This process is called channel transition.We denote the number of new patching channels with x′

ij . LetPC′

i be the new patching channel used by an ONU scheduler.Define xij = xij − x′

ij . If xij > 0, the PBFP schememust assign more patching channels to video i. This case iscalled positive channel transition. If xij < 0, some patchingchannels must be released from video i and assigned to othervideos. This case is called negative channel transition.

An ONU scheduler partitions a video into yi × (2x′ij − 1)

segments. The length of each segment is δ′ = Li

yi×(2x′ij −1)

. The

ONU scheduler determines the transition time point Tp for

Fig. 6. Negative channel transition procedure in PBFP.

video i to be the beginning of transmitting whole video at thefirst broadcasting channel. For example in Fig 2, transitiontime point for video can be at time units 0, 0 + Li, 0 + 2Li andso on. Let PC′

u (where 1 ≤ u ≤ x′ij) is assigned to transmit

the new segment.The process of negative channel transition can be performed

by the following steps.Step 1: Let PC′

1 match with old channel PC1 + xij . Start-ing from Tp, patching channel PC′

1 periodically broadcastssegment S′

1.Step 2: The patching channel PC′

u (where u = 2, . . . , x′ij)

periodically broadcasts new segments S′u−12 to S′u

2−1 startingfrom TP + (2u−1−1) × δ′.

Fig. 6 shows how to transit from xij = 5 channels to x′ij = 3

channels. The transition time point for this video is determinedat time Tp. Channel PC′

1 starts to broadcast S′1 at time Tp.

Segments S′2and S′

3 will be broadcast via channel PC′2 at time

Tp+δ’. Channel PC′3will broadcast segments S′

4 to S′7 at time

Tp+3×δ’.An ONU scheduler first performs negative channel transi-

tion for videos that their popularities have decreased. Then, itstarts the positive channel transition for those videos that theirnumber of allocated patching channels have increased. Thereleased channels will be used for positive channel transition.At the ONU side, the following steps are involved in positivechannel transition.

Step 1: At the beginning of time Tp, add xij patchingchannels, and use the new channel set to patch the initial videosegments.

Step 2: Starting from time Tp, use channel PC′1 to broadcast

segment S′1 periodically.

Step 3: On each patching channel PC′u, where (u =

2, . . . , x′ij), periodically broadcast segments S′u−1

2 to S′u2−1

based on new configuration starting from TP + (2u−1−1) × δ′.Fig. 7 demonstrates how to transit the patching channels

from xij = 3 channels to x′ij = 4 channels. In this exam-

ple, the transition point occurs at time Tp. Channels PC′1

and PC′2 start periodically broadcasting from time Tp and

Tp + δ′, respectively. The remaining channels switch to newconfiguration after time δ’ time units. The clients who requestservice after transition point receive new segments based onthe new configuration. For the clients receiving service before


Fig. 7. Positive channel transition procedure in PBFP.

transition point, the work in [44] has proved that the endingtime of old segments always occurs before the starting timeof new segments.

V. Performance Evaluation

In this section, the proposed BFP and PBFP schemes areevaluated on Shared-WDM-PON using the OPNET modeler[50]. First, we compare the performance of the BFP andPBFP schemes with CBP in terms of WPD and averageplayback delay (APD). The rest of this section is allocatedto the performance evaluation of each proposed scheme undervarious video request rates.

We assume that in total M = 10 video objects, each witha length of L = 120 min, are stored in a video server. Therequest arrival at an ONU follows a Poisson distribution withaverage rate �. Research shows that the popularity distributionof video titles follows the Zipf distribution [51]. The requestarrival rate of video i at ONU j is derived as

�ij =�

((M + 1) − idij)θM∑j=1

1

jθ

where idij ∈ [1, 10] is the interest degree of video i at ONU juniformly distributed between 1 and 10. We choose the skewfactor θ = 0.271 according to the studies in [6].

We assume that the Shared-WDM-PON supports 16 ONUs.The bandwidth of ω0 is considered to be 2.488 Gbps. The bitrate of each broadcasting/patching channel is considered tobe 1.5 Mbps. We assume that each user playbacks its videofor a time duration that its length is uniformly distributed inbetween 1 and 120 min. The duration of each simulation runis 1680 h (70 days). Simulation results are found with 95%of confidence intervals to be within ±1% of the mean valuesshown. Each simulation is replicated ten times. The systemparameters are depicted in Table I.

A. Effect of OLT Broadcasting Channels

Assume that in total 40 patching channels are availableat an ONU for patching. We first compare the performanceof BFP, PBFP, and CBP in terms of average WPD. Fig. 8compares the average WPD under the CBP, BFP, and PBFPschemes as the total number of broadcasting channels allocated

TABLE I

Simulation Parameters

Parameter Default value RangeONU capacity in logical channels 60 20–60OLT capacity in logical channels 70 30–70Data rate of each channel in Mbps 1.5 N/ANumber of video titles 10 N/AVideo length in minute 120 N/ANumber of ONUs 16 N/ASkew factor 0.271 N/ARequest rate per minute 3 0.1−100

Bracket a root in bisection method a 10−8 N/ABracket a root in bisection method b 5 N/A

Convergence tolerance, |pn – p| 10−8 N/A

Fig. 8. Average WPD versus the number of available broadcasting channels.

to the OLT varies from 30 to 70. The average arrival rateof client requests is three requests per minute. We see thatBFP performs better than CBP under all numbers of availablebroadcasting channels. Applying the ONU fast patching forpatching (as done in BFP) decreases the WPD at each ONU.Furthermore, we can see from this figure that PBFP providesimprovement over CBP and BFP in all ranges of broadcastingchannels. Compared with CBP, WPD is almost halved. This isbecause PBFP dedicates appropriate number of broadcastingchannels for each video title according to its popularity. As aresult, clients experience low WPD. In particular, Fig. 8 showsthat when the number of broadcasting channels increases, theaverage WPD declines.

We also investigate the performance of BFP, PBFP, interms of APD. Fig. 9 depicts the APD as a function ofthe number of available broadcasting channels. As shown inFig. 9, APD under the PBFP scheme is about one half ofthe CBP scheme. This shows that our PBFP scheme, whileimplementing seamless channel transition, can provide betterperformance results than the other schemes.

B. Effect of ONU Patching Channels

Fig. 10 demonstrates the impact of the number of patchingchannels on performance of the proposed schemes. In the fol-lowing simulations, the number of broadcasting channels is setto 50. Compared with two other techniques, the performanceof BFP degrades when the number of patching channels is


Fig. 9. APD versus the number of available broadcasting channels at theOLT.

Fig. 10. Average WPD versus the number of available patching channels.

less than 30 channels. This is because of the fact that thesize of video segments transmitted at patching window byBFP increases when the number of available patching channelsis low. Because of delivering video segments with large sizealong patching channel, WPD will increase. This problem isnot observed in CBP because the ONU scheduler in CBPdivides the patching window into smaller size segments atlow number of patching channels. Furthermore, it is shownin Fig. 10 that BFP and PBFP increases the performanceconsiderably and maintains good performance at any patchingchannels beyond 30 channels.

We see that as the number of patching channels increasesfrom 25 to 55, the PBFP outperforms BFP in terms of APD.This is due to the fact that PBFP determines the number ofbroadcasting channels and patching channels based on predic-tion popularity of each video. However, when the number ofpatching channels is high, APD in BFP and PBFP is almostequal.

Fig. 11 shows APD as a function of the number of patchingchannels. The PBFP provides good performance to declineAPD compared with CBP and BFP when the number ofpatching channel is larger than 30 channels. Although BFP canprovide small APD and WPD when the number of patchingchannels is high, the delay performance of BFP is quitehigh when each ONU has low patching channels, which is adrawback of the BFP scheme. Due to the explosive growth of

Fig. 11. APD versus the number of available patching channels.

Fig. 12. Effect of request arrival rate on BFP.

Fig. 13. Effect of request arrival rate on PBFP.

multimedia applications, increasing bandwidth is unavoidablein PONs in the future. Increasing link speed should happen atthe ONU side. Hence, an ONU should provide more logicalchannels specifically for video delivery. In this case, BFP canbe a more efficient mechanism for video delivery than others.

C. Effect of Request Rate

Here, the effect of request arrival rate � in BFP is evaluated.Fig. 12 illustrates the behavior of BFP under different patchingchannels at ONUs, i.e., we vary the patching channels from20 to 60. The performances of BFP under different arrival


rates become close to each other by increasing the numberof patching channels. At a high number of available patchingchannels, performance of the BFP is independent of the arrivalrates of requests. Due to the broadcasting nature of ourproposed schemes, increasing the arrival rate has no effect onAPD when the number of available patching channels is high.Thus, this approach is appropriate for more popular videos.

We have obtained APD for different numbers of availablepatching channels under the PBFP scheme by taking requestarrival rates � = 0.1, 1, 3, 10, and 100. Fig. 13 shows APDversus the number of patching channels. The APD goes upby increasing the arrival rate under any number of patchingchannels. This is because the PBFP scheme must serve a largenumber of requests in a time slot. As a result, the systemperformance degrades and clients experience large playbackdelay. In particular, as the number of patching channelsincreases from 20 to 60, increasing the request arrival ratehas a slight effect on APD.

VI. Conclusion

In this paper, two video delivery schemes for providingVoD service over Shared-WDM-PON systems were proposedand evaluated. We proposed the BFP scheme that utilized theONU fast patching scheme at each ONU for patching function.In addition, we developed an optimization problem regardingoptimal allocation of available logical channels at OLT andONUs that led to minimum WPD. A heuristic algorithm to findnear optimum solutions of related optimization problem wasproposed. Simulation results showed that BFP can increase theperformance considerably and can maintain good performancewhen the number of available patching channels was high.

The major characteristic of the PBFP scheme was the priorprediction of video popularity at ONUs and adjusting the num-bers of channel allocated to videos seamlessly. Using predictorfor video popularity and channel transition mechanism wasefficient for utilization of available bandwidth at ONUs andOLT. This is due to the fact that prediction of users’ behaviorallows the OLT scheduler to assign more bandwidth to sendmore popular videos earlier than their requests by clients. ThePBFP scheme can provide good improvement over BFP.

References

[1] G. O’Driscoll, Next Generation IPTV Services and Technologies. NewYork, NY, USA: Wiley-Interscience, 2008.

[2] S. W. Carter and D. D. E. Long, “Improving bandwidth efficiency ofvideo-on-demand servers,” Comput. Netw., vol. 31, pp. 111–123, Jan.1999.

[3] G. Kramer, B. Mukherjee, S. Dixit, Y. Ye, and R. Hirth, “Supportingdifferentiated classes of service in Ethernet passive optical networks,” J.Opt. Netw., vol. 1, pp. 280–298, Aug. 2002.

[4] Gigabit-Capable Passive Optical Networks (GPON): General Charac-teristics, ITU-T G.984.1, 2008.

[5] N. U. Kim and M. Kang, “Traffic share-based multicast scheduling forbroadcast video delivery in shared-WDM-PONs,” J. Lightw. Technol.,vol. 25, pp. 2814–2827, Sep. 2007.

[6] A. Dan, D. Sitaram, and P. Shahabuddin, “Scheduling policies for anon-demand video server with batching,” in Proc. 2th ACM Int. Conf.Multimedia, 1994, pp. 15–23.

[7] K. A. Hua, J. H. Oh, and K. Vu, “An adaptive hybrid technique forvideo multicast,” in Proc. 7th Int. Conf. Computer Communications andNetworks, 1998, pp. 227–234.

[8] N. Sarhan and C. Das, “A new class of scheduling policies forproviding time of service guarantees in video-on-demand servers,”Manage. Multimedia Networks Services, vol. 3271, pp. 199–236,Oct. 2004.

[9] V. Aggarwal, R. Caldebank, V. Gopalakrishnan, R. Jana, K. Ramakrish-nan, and F. Yu, “The effectiveness of intelligent scheduling for multicastvideo-on-demand,” in Proc. 17th ACM Int. Conf. Multimedia, 2009,pp. 421–430.

[10] M. Jain, V. Sharma, and K. Priya, “Adaptive batching scheme formulticast near video-on-demand (NVOD) system,” J. ICT, vol. 5, no.1, pp. 1–12, 2006.

[11] W. Wen, S. H. G. Chan, and B. Mukherjee, “Token-tray/weightedqueuing-time (TT/WQT): An adaptive batching policy for near video-on-demand system,” Comput. Commun., vol. 25, no. 9 pp. 890–904, Jun.2002.

[12] K. A. Hua, Y. Cai, and S. Sheu, “Patching: A multicast technique for truevideo-on-demand services,” in Proc. 6th ACM Int. Conf. Multimedia,1998, pp. 191–200.

[13] L. Gao and D. Towsley, “Supplying instantaneous video-on-demandservices using controlled multicast,” in Proc. IEEE Int. Conf. MultimediaComput. Syst., vol. 2. 1999, pp. 117–121.

[14] Y. Cai, W. Tavanapong, and K. A. Hua, “A double patching technique forefficient bandwidth sharing in video-on-demand systems,” MultimediaTools Appl., vol. 32, no. 1, pp. 115–136, Jan. 2007.

[15] H. Hlavacs and S. Buchinger, “Hierarchical video patching with optimalserver bandwidth,” ACM Trans. Multimedia Comput. Commun. Appl.,vol. 4, pp. 1–23, Jan. 2008.

[16] L. Gao and D. Towsley, “Threshold-based multicast for continuousmedia delivery,” IEEE Trans. Multimedia, vol. 3, no. 4, pp. 405–414,Dec. 2001.

[17] K. Sato, M. Katsumoto, and T. Miki, “Fragmented patching: New VODtechnique that supports client mobility,” in Proc. 19th Int. Conf. AINA,Mar. 2005, pp. 527–532.

[18] S. Chand, B. Kumar, and H. Om, “Video data delivery using slottedpatching,” J. Network Comput. Appl., vol. 32, no. 3, pp. 660–665, May2009.

[19] E. Song, H. Kim, and S. Park, “Fast patching scheme for Video-on-Demand service,” in Proc. Asia-Pacific Conf. Commun., Bangkok, Oct.2007, pp. 523–526.

[20] A. Bar-Noy and R. E. Ladner, “Competitive on-line stream mergingalgorithms for media-on-demand,” in Proc. 12th Annu. ACM-SIAMSymp. Discrete Algorithms, 2001, pp. 364–373.

[21] A. Bar-Noy, J. Goshi, R. E. Ladner, and K. Tam, “Comparison of streammerging algorithms for media-on-demand,” Multimedia Syst., vol. 9, no.5, pp. 411–423, Mar. 2004.

[22] D. Eager, M. Vernon, and J. Zahorjan, “Optimal and efficient mergingschedules for video-on-demand servers,” in Proc. 7th ACM Int. Conf.Multimedia (Part 1), 1999, pp. 199–202.

[23] S. W. Carter, D. D. E. Long, and J. F. Paris, Video-on-DemandBroadcasting Protocols. San Diego, CA, USA: Academic Press,2000.

[24] H. Jin, D. Deng, and L. Pang, “Medusa: A novel stream-schedulingscheme for parallel video servers,” EURASIP J. Appl. Signal Process.,vol. 2004, no. 2, pp. 317–329, Mar. 2004.

[25] L. S. Juhn and L. M. Tseng, “Fast data broadcasting and receivingscheme for popular video service,” IEEE Trans. Broadcast., vol. 44,no. 1, pp. 100–105, Mar. 1998.

[26] K. A. Hua and S. Sheu, “Skyscraper broadcasting: A new broadcastingscheme for metropolitan video-on-demand systems,” in Proc. ACM SIG-COMM Conf. Appl. Technol. Architectures Protocols Comput. Commun.,1997, pp. 89–100.

[27] S. Viswanathan and T. Imielinski, “Metropolitan area video-on-demandservice using pyramid broadcasting,” Multimedia Syst., vol. 4, no. 4,pp. 197–208, Aug. 1996.

[28] S. W. C. J. F. Paris and D. D. E. Long, “A hybrid broadcasting protocolfor video on demand,” in Proc. Multimedia Comput. Networking Conf.,Jan. 1999, pp. 317–326.

[29] L. S. Juhn and L. M. Tseng, “Harmonic broadcasting for video-on-demand service,” IEEE Trans. Broadcast., vol. 43, no. 3, pp. 268–271,Sep. 1997.

[30] J. F. Paris, S. W. Carter, and D. D. E. Long, “Efficient broadcastingprotocols for video on demand,” in Proc. 6th Int. Symp. Modeling Anal.Simulation Comput. Telecommun. Syst., 1998, pp. 127–132.

[31] A. Hu, I. Nikolaidis, and P. Van Beek, “On the design of ef-ficient video-on-demand broadcast schedules,” in Proc. 7th Int.Symp. Modeling Anal. Simulation Comput. Telecommun. Syst., 1999,pp. 262–269.


[32] M. E. Yan and T. Kameda, “Generalized Fibonacci broadcasting: Anefficient VOD scheme with user bandwidth limit,” Discrete Appl. Math.,vol. 154, no. 16, pp. 2418–2429, Nov. 2006.

[33] C. J. Wu, Y. W. Chen, J. H. Yan, and Y. L. Wang, “Seamless channeltransition for slotted generalized fibonacci broadcasting,” MultimediaSyst., vol. 13, no. 3, pp. 223–233, Sep. 2007.

[34] T. Kameda and Y. Sun, “Survey on VOD broadcasting schemes,” Schoolof Computing Science, SFU, 2003.

[35] Y. C. Tseng, M. H. Yang, C. M. Hsieh, W. H. Liao, and J. P. Sheu,“Data broadcasting and seamless channel transition for highly demandedvideos,” IEEE Trans. Commun., vol. 49, no. 5, pp. 863–874, May 2001.

[36] H. F. Yu, H. C. Yang, Y. M. Chen, L. M. Tseng, and C. Y. Kuo, “Smoothfast broadcasting (SFB) for compressed videos,” Wired/Wireless InternetCommun., (WWIC2004), LNCS. 2957, pp. 272-283, Frankfurt (Oder),Germany, Feb. 2004.

[37] H. F. Yu, H. C. Yang, and L. M. Tseng, “Reverse fast broadcasting(RFB) for video-on-demand applications,” IEEE Trans. Broadcast., vol.53, no. 1, pp. 103–111, Mar. 2007.

[38] J. Wang, C. Qiao, Y. Li, and K. Lu, “On guaranteed VoD services in nextgeneration optical access networks,” IEEE J. Select. Areas Commun.,vol. 28, no. 6, pp. 875–888, Aug. 2010.

[39] Y. Xu and Y. Li, “ONU patching for efficient VoD service over integratedfiber-wireless (FiWi) access networks,” in Proc. 6th Int. ICST Conf.Commun. Networking China (CHINACOM), 2011, pp. 1002–1007.

[40] L. Ardissono, C. Gena, P. Torasso, F. Bellifemine, A. Difino, and B.Negro, “User modeling and recommendation techniques for personalizedelectronic program guides,” in Personalized Digital Television: TargetingPrograms to Individual Viewers, Boston, MA, USA: Kluwer Academic,2004, pp. 3–26.

[41] S. Xiaowei, “An intelligent recommendation system based on fuzzylogic,” in Proc. 1st Int. Conf. Inform. Contr., Autom. Robot., Aug. 2004,pp. 180–184.

[42] D. Deng, H. Jin, and Z. Han, “A dynamically grouped multi-multicaststream scheduling strategy for video-on-demand systems,” in Proc.Comput. Sci. ICCS, vol. 2658. 2003, pp. 822–831.

[43] L. Gao, Z. L. Zhang, and D. Towsley, “Proxy-assisted techniques fordelivering continuous multimedia streams,” IEEE/ACM Trans. Netw.,vol. 11, no. 6, pp. 884–894, Dec. 2003.

[44] W. D. Chien, Y. S. Yeh, and J. S. Wang, “Practical channel transitionfor near-VOD services,” IEEE Trans. Broadcast., vol. 51, no. 3, pp.360–365, Sep. 2005.

[45] S. Ramesh, I. Rhee, and K. Guo, “Multicast with cache (Mcache):An adaptive zero-delay video-on-demand service,” IEEE Trans. CircuitsSyst. Video Technol., vol. 11, no. 3, pp. 440–456, Mar. 2001.

[46] C. Zhang, D. Liu, L. Zhang, and G. Wu, “Using IGMP V3 forcontrollable multicast over EPON,” in Proc. SPIE, 2008, p. 71373Z.

[47] J. F. Paris and D. D. E. Long, “Limiting the client bandwidth ofbroadcasting protocols for video-on-demand,” in Proc. Euromedia Conf.,2000, pp. 107–111.

[48] M. S. Bazaraa, H. D. Sherali, and C. M. Shetty, Nonlinear Programming:Theory and Algorithms, 3rd ed. Wiley Online Library, 2006.

[49] R. L. Burden and J. D. Faires, Numerical Analysis, 9th ed. Boston, MA,USA: Brooks/cole, 2010.

[50] OPNET Technologies Inc., OPNET Modeler [Online]. Available: http://www.opnet.com (accessed 2012)

[51] W. Shu and M. Y. Wu, “Resource requirements of closed-loop videodelivery services,” IEEE Multimedia, vol. 11, pp. 24–37, 2004.

Sepideh Nikmanzar received the B.Sc. degree ininformation technology engineering from QazvinIslamic Azad University, Qazvin, Iran, in 2008, andthe M.Sc degree in information technology engi-neering from the Sahand University of Technology,Sahand New Town, Tabriz, Iran.

Her current research interests include multimediadelivery, Internet protocol television, optical net-works, and metaheuristic algorithms.

Akbar Ghaffarpour Rahbar (M’04–SM’12) re-ceived the B.Sc. and M.Sc. degrees in computerhardware and computer architecture from the IranUniversity of Science and Technology, Tehran, Iran,in 1992 and 1995, respectively, and the Ph.D. degreein computer science from the University of Ottawa,Ottawa, ON, Canada, in 2006.

He is currently an Associate Professor with theElectrical Engineering Department, Sahand Univer-sity of Technology, Sahand New Town, Tabriz,Iran. He is the Director of the Computer Net-

works Research Laboratory, Sahand University, Iran. His current researchinterests include optical networks, optical packet switching, scheduling,PON, IPTV, VANET, network modeling, analysis and performance eval-uation, the results of which can be found in over 80 technical papers(http://ee.sut.ac.ir/showcvdetail.aspx?id=13).

Dr. Rahbar is currently on the Editorial Board of the Wiley Transactionson Emerging Telecommunications Technologies Journal, the InternationalJournal of Advances in Optical Communication and Networks, and the Journalof Convergence Information Technology.

Amin Ebrahimzadeh received the B.Sc. and M.Sc.degrees in electrical engineering from the Universityof Tabriz, Tabriz, Iran, in 2009 and 2011, respec-tively. He is currently pursuing the Ph.D. degreeat the Department of Electrical Engineering, Sa-hand University of Technology, Sahand New Town,Tabriz.

His current research interests include opticalWDM networks, network optimization, wireless sen-sor networks, and image transmission.

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