QoS-Aware Dynamic Adaptation for Cooperative Media Streaming in Mobile Environments

QoS-Aware Dynamic Adaptation forCooperative Media Streaming in

Mobile EnvironmentsShiow-yang Wu, Member, IEEE, and Cheng-en He

Abstract—Media streaming is expected to be one of the most promising services in mobile environments. Effective data streaming

management techniques are, therefore, in strong demand. In an earlier paper, the ideas and benefits of two-level cooperative media

streaming with headlight prefetching and dynamic chaining were demonstrated [1]. Though complementary to each other, they operate

in session-wide static and distinctive modes. Moreover, users do not have control over the quality and cost levels of the streaming

services. The performance degradation or cost increment can reach an unacceptable level under fast or highly unstable moving

patterns. In this paper, we propose the QoS-based dynamic adaptation techniques for the flexible employment and smooth integration

of headlight prefetching and dynamic chaining to continuously provide quality streaming services to mobile users. The QoS-aware

dynamic headlight prefetching is for the cooperation between streaming access points to dynamically adjust the prefetching scheme in

response to the fast changing moving patterns. Adaptive P2P media streaming is for the cooperation between mobile users such that

multiple peers can be used as streaming sources to increase the likelihood of successful chaining. Furthermore, a QoS-based

technique is developed to dynamically trigger and proportionally adjust the prefetching degree when the stability and quality of P2P

streaming service vary. With extensive simulation and performance evaluation, we demonstrate that the proposed dynamic adaptation

techniques significantly improve the service quality and streaming performance of cooperative media streaming in mobile

environments.

Index Terms—Dynamic headlight prefetching, P2P media streaming, QoS-aware adaptation, mobile data management.

Ç

1 INTRODUCTION

EFFECTIVE data management for mobile media streamingis becoming more and more important with the

proliferation of wireless media services. Seamless mediastreaming in mobile environments is especially challengingfor several respects. Wireless communication bandwidth is,in general, a scare and unstable resource. Link and nodefailures or terrain blockage can result in periods ofintermittent connectivity. For media streaming applications,such a harsh environment often leads to undesirabledisconnections and playback interruptions. The continuousplaying requirement of streaming medias only add to thedifficulty of the situation. To remedy these problems, it isusually necessary to incorporate certain degree of prefetch-ing and caching to transmit the media segments before theyare requested. However, user mobility significantly com-plicates the problem since the exact locations and targets foreffective prefetching and caching are very difficult todetermine reliably.

In an earlier paper, the ideas and benefits of two-level

cooperative media streaming with headlight prefetching

and dynamic chaining were successfully demonstrated [1].Headlight prefetching facilitates the cooperation of stream-ing access points (SAPs) to cope with the uncertainty of usermovement. For each mobile user, a virtual fan-shapedheadlight prefetching zone is maintained along the direc-tion of movement. The overlapping area of the headlightzone on a particular cell and the accumulated virtualilluminance determine the degree and volume of prefetch-ing. Dynamic chaining is for the cooperation among mobileusers to maximize cache utilization and streaming benefit.On a media-segment request, a search for a supplyingpartner, who happens to be viewing the same media, isinstantiated. Then, the requesting user can be chained to thesupplying partner to receive subsequent segments directlywithout further intervention. The user can itself be asupplying partner for other users and naturally form achain of users that are viewing and sharing the same media.As complementary techniques, headlight prefetching anddynamic chaining are quite effective for mobile mediastreaming. However, they operate in session-wide staticand distinctive modes. All prefetching-related settings arefixed until the next round of prefetching. Media segmentsare supplied either by chaining from a peer or byprefetching from the SAP. Moreover, the users do not havecontrol over the cost and quality levels of the streamingservices. The performance of both headlight prefetchingand dynamic chaining can drop significantly under fastchanging moving patterns.

In this paper, we propose QoS-based dynamic adapta-tion techniques for the flexible employment and smoothintegration of headlight prefetching and dynamic chaining

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011 439

. S.-y. Wu is with the Department of Computer Science and InformationEngineering, National Dong Hwa University, Hualien, Taiwan, ROC.E-mail: [email protected].

. C.-e. He is with the R&D, InfoPower, 3F-2, No. 3, Park Street, Building F,Nankang Software Park, Taipei, 115, Taiwan, ROC.E-mail: [email protected].

Manuscript received 28 May 2009; revised 4 Sept. 2009; accepted 4 Sept.2009; published online 5 Apr. 2010.Recommended for acceptance by A. Boukerche.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TPDS-2009-05-0244.Digital Object Identifier no. 10.1109/TPDS.2010.81.

1045-9219/11/$26.00 � 2011 IEEE Published by the IEEE Computer Society

to continuously provide quality-streaming services tomobile users. To cope with the deficiencies of previoustechniques, QoS-aware dynamic headlight prefetching is pro-posed to dynamically adjust the prefetching scheme inresponse to the fast changing or unstable moving patterns.More specifically, key QoS parameters and cost factors areidentified and formulated to characterize the current statusof the prefetching and streaming services. By continuouslymonitoring the QoS and cost variations, we can identify theexact timing and targets for adaptation. Proper adaptationstrategies can then be devised based on the attempt toremedy the drop in quality or increase in processing cost.With the QoS-aware techniques, we can unify the headlightprefetching and service adaptation into an integratedscheme, which makes it possible to continuously providehigh-quality services with lower cost.

Adaptive P2P media streaming, on the other hand, is for thecooperation between mobile users such that multiplestreaming sources from different partners can be used toincrease the likelihood of successful chaining. Morespecifically, instead of solely relying on one supplyingpartner for media segments as in previous approach, wemaintain a supplying partner set which consists of relativelystable peers who can provide the needed segments. Thestreaming service is then split among the supplyingpartners to speed up download time and improve stabilityat the same time.

In addition to the adaptive-streaming techniques, a QoS-based scheme is developed to dynamically trigger andproportionally adjust the prefetching degree when thestability and quality of P2P streaming service vary. Thetechnique facilitates smooth integration of headlight pre-fetching and dynamic chaining to continuously providequality-streaming services to mobile users. Extensivesimulation and performance evaluation results demonstratethat the proposed QoS-based dynamic-adaptation techni-ques significantly improve the service quality and stream-ing performance of cooperative media streaming in mobileenvironments.

The rest of the paper is organized as follows: Section 2provides a survey of related research work. In Section 3, wereview the two-level streaming architecture, headlightprefetching as well as dynamic chaining techniques pro-posed in [1] to make this paper self-contained. In Section 4,we introduce the QoS mechanism and the dynamic head-light prefetching techniques. In Section 5, we detail theadaptive P2P media streaming and chaining techniques foruser-level cooperation. The techniques for the dynamicintegration of headlight prefetching and P2P chaining ispresented in Section 6. In Section 7, we outline thesimulation environment and present the results of compara-tive performance evaluation. Section 8 concludes the paper.

2 RELATED WORK

Media-streaming schemes have been discussed on wirednetworks for quite some time [2], [3], [4]. Cooperative media-streaming techniques in peer-to-peer like environments aregetting popular in recent years for its scalability and potentialcost saving [5], [6], [7], [8], [9], [10], [11]. Media streaming inmobile environments, in particular, has been attracting much

attention lately [12]. A real-time continuous media streamingprotocol is proposed by Fitzek and Reisslein with specialemphasis on dynamic transmission capacity allocation andprefetching [13]. The NonStop middleware developed by Liand Wang employs partition prediction and service replica-tion for continuous media streaming in mobile and ad hocnetworks [14]. Anastasi et al. offer detailed consideration ofenergy efficiency when providing streaming media servicesto mobile clients [15]. Tong et al. analyze the cost effective-ness of video streaming over multihop wireless networks,especially on the use of path diversity [16]. Kyriakidou et al.investigate video streaming techniques in 3G mobile net-works on top of a three-tier architecture [17]. This architec-ture is similar to our model. However, they assume aclustered infrastructure which is not needed in our work.Moreover, their emphasis is on the coordination of mediaservers for smooth handover and the rate-adaptationtechnique for load balancing. There is no coordination atthe base station level or the user level as we do in this paper.Data management issues are largely ignored.

Cooperative mobile streaming techniques also begin toemerge in the past few years. Xue et al. explore groupmobility for predicting future availability of wireless linksto increase total streaming capacity [18]. Qin et al. proposean algorithm to predict continuous link availability betweenmobile users [19]. The V3 architecture proposed by Guo etal. for live video streaming is a cooperative streamingarchitecture among moving vehicles [20]. Broadcasting andmulticasting have also been used for streaming mediaservices to mobile users [21], [22]. However, the receiversare required to be connected and tuned at the right channelof broadcasting or multicasting. Repeated broadcasting ormulticasting may be unavoidable to satisfy ad hoc requeststo the same media at different times and locations.

The adoption of QoS concept in mobile computing hasbeen quite popular, too. Soh and Kim proposed early methodfor QoS provisioning in cellular networks [23]. An adaptiveframework was proposed by Kwon et al. for multimediaservices with QoS support in mobile networks [24]. Instead ofcontrolling QoS across communication link, Guenkova-Luyet al. offered end-to-end QoS coordination schemes formobile-multimedia applications [25]. An interesting ap-proach using reinforcement learning was given by Fei et al.on adaptive multimedia with QoS support in mobile net-works [26]. The WiQoSM model proposed by Resta and Santiis a QoS-based integrated model for mobility and userbehavior modeling in mobile environments [27]. Ourapproach differs from other works in that we employ QoSmechanisms for devising adaptation techniques on predic-tive data management as well as quality streaming services.

Data-stream management systems are also related to ourwork on the need to process continuously arrival datastream(s) [28], [29], [30]. Distributed stream-processingsystems, in particular, are closely related since most ofthese systems are designed for disseminating streamingdata from sources to distributed destinations. Zhou et al.proposed an adaptive and cost-based approach for con-structing dissemination trees to minimize the average lossof fidelity [31]. The Synergy distributed stream processingmiddleware proposed by Repantis et al. allows the sharing

440 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 22, NO. 3, MARCH 2011

of processing components and result streams with QoSawareness [32]. These techniques, however, cannot be easilyapplied to our work since most of them do not consider thedissemination of streaming data to mobile clients.

3 HEADLIGHT PREFETCHING AND DYNAMIC

CHAINING

To make this paper self-contained, we provide a shortreview of the key techniques introduced in [1]. The servicearchitecture consists of streaming media servers (SMSs), SAPs,and mobile users. The SAP-level cooperation is to facilitateheadlight prefetching for proper data management andseamless handoff. Dynamic-chaining techniques form user-level P2P cooperation to share media segments with eachothers.

Headlight prefetching is to maintain a headlight prefetch-ing zone as depicted in Fig. 1 along the direction of usermovement to serve as a prediction of possible futureinteraction with neighboring cells, and also to helpproviding predictive prefetching services. The radius rdetermines the extent of look ahead. The angle � controlsthe span of coverage. All SAPs of the cells overlapped withthe zone are selected as the prefetching SAPs. The SAPsfarther away are assigned fewer segments to save the cost.The SAPs closer by are allocated with more segments toprevent undesirable interruption. The accumulated virtualilluminance on a cell is used to determine the segmentassignment. To deal with changing mobility patterns,headlight shifting and headlight sharing are used to improvethe level of cooperation among SAPs. Headlight prefetchingis shown to significantly improve the performance ofstreaming services in comparison with on-demand servicesbased on simple lookahead prefetching. However, in theface of fast changing or highly unstable moving patterns,headlight prefetching may suffer from sudden increase inplayback interruptions and segment download time.

Dynamic chaining is to explore client storage and user-level cooperation to facilitate seamless streaming serviceseven when disconnected from the SAP. On a segmentrequest, a search for a supplying partner among theneighboring peers viewing the same media is initiated.Once located, the requesting client is chained after thefound peer to receive the streaming segments in a P2P

fashion. The viewed segments can be shared with otherpeers in similar way to form chains of peers. Since the costof the user level P2P streaming is much lower than thedirect service from the SAP, dynamic chaining provideseven more saving when applied with headlight prefetching.However, a client is chained to only one supplier whichmay be unstable. Even with belt sharing to quickly identifyan alternative supplier, frequent breakage of the chain canstill incur excessive overhead.

We propose techniques to overcome the deficienciesdiscussed above. In particular, we introduce QoS andadaptive processing concepts for improving the reliabilityof prefetching as well as increasing the chance of successfulchaining.

4 QOS-AWARE DYNAMIC HEADLIGHT

PREFETCHING

The dynamic headlight prefetching techniques proposed inthis section are designed to cope with the difficulty of simpleheadlight prefetching on the services to mobile users withfast changing and unstable moving patterns. In particular,we tackle the problem by a QoS-based approach thatcontinuously strive for a good balance between servicequality and processing cost. On sudden drop in quality orsignificant increase in cost, adaptation actions are triggeredto change the shape and size of the headlight zone as well asthe prefetching strategies. The goal is to continuouslymaintain the service quality above a user-defined minimallevel while keeping the cost below a prespecified upper limit.

4.1 QoS Measure

We propose a QoS mechanism to characterize the level ofquality for headlight prefetching. In particular, we areinterested in the precision, ratio, and response time of theservices.

A prefetched segment may not be actually consumed bythe user due to, for examples, sudden change of movingpattern, disconnection, etc. In such cases, all the effort spenton the prefetching of the segment is in vain. Prefetchprecision is, therefore, defined as the ratio of media segmentsthat are actually used within the prefetched segments. Morespecifically, let Su be the number of prefetched segmentsand Sp be the number of prefetched segments that areactually used, then the prefetch precision P is defined as

P ¼ SpSu: ð1Þ

Obviously, the higher the precision the better the prefetch-ing technique.

A prefetching technique with high precision is of little useif the prefetched segments account for only a small portionof the requested segments. We, therefore, define the prefetchratio as the ratio of media segments that are served usingheadlight prefetching with respect to the total number ofrequested segments. More specifically, let Sr be the numberof requested segments within a fixed period of time and Spbe the number of prefetched segments that are actually usedwithin that period, then the prefetch ratio R is defined as

R ¼ SpSr: ð2Þ

WU AND HE: QOS-AWARE DYNAMIC ADAPTATION FOR COOPERATIVE MEDIA STREAMING IN MOBILE ENVIRONMENTS 441

Fig. 1. The headlight prefetching zone.

Naturally, a prefetching technique must achieve highprecision and prefetch ratio at the same time to be effective.This can be characterized by the Harmonic mean of P and Rwhich is defined to be the prefetching effectiveness qualitymeasure Qe as follows:

Qe ¼2

1P þ 1

R

¼ 2PR

P þR : ð3Þ

Even with the value of Qe approaching 1, an effectivetechnique may not be an efficient one. We want a techniquewith short service response time, which is defined as theaverage time between requesting and actually receiving therequested segments. Note that for those requested segmentsthat are successfully prefetched, only a transmission fromthe local SAP to the mobile user is needed. For thosesegments that are not prefetched, we must fetch them fromother SAPs or from the remote SMSs. Let the local segmenttransmission time from an SAP to a mobile user be d, thetransmission time between SAPs be l, and the averagenumber of hops a segment may travel before reaching thedestination be h, then the response time T can bedetermined by

T ¼ dSp þ ðdþ lhÞðSr � SpÞ; ð4Þ

where Sr and Sp are defined as before. With simplealgebraic manipulation, the equation can be simplified into

T ¼ ðdþ lhÞSr � lhSp: ð5Þ

To convert the response time into a quality measurebetween 0 and 1, we consider the best and worst cases. Inthe worst case, no segment is served with prefetching (i.e.,Sp ¼ 0Þ. The worst case response time Tw can be determinedas follows:

Tw ¼ ðdþ lhÞSr: ð6Þ

In the best case, all requested segments are successfullyprefetched, i.e., Sp ¼ Sr. Then, the best case response time Tbcan be formulated as

Tb ¼ dSr: ð7Þ

The response time quality measure Qt can, therefore, bedefined as

Qt ¼Tw � TTw � Tb

; ð8Þ

where T is the measured response time.To combine the Qe and Qt measures, we can again take

the Harmonic mean to have the headlight prefetching qualitymeasure Q as follows:

Q ¼ 2QeQt

Qe þQt: ð9Þ

With the QoS definitions above, a user can now specify adesired minimal level of quality Qlower which is consideredas the lower bound. In other words, the goal is to maintainthe level of quality Q such that

Q > Qlower ð10Þ

is satisfied at all time.

4.2 Cost Measure

Quality measures tell only half of the story. Blindly seekingof high quality may significantly increase the resourceconsumption and system workload. Furthermore, the costof achieving certain level of quality may be too high to bepractical. We, therefore, need a good cost measure tobalance the quality.

There are two main cost factors in headlight prefetchingand media services. The first one is the cost of sendingprefetching messages to the SAPs within the currentheadlight zone. The message transmission cost is propor-tional to the distance between a prefetching SAP and thelocal SAP. Another factor is the cost of sending mediasegments across the network to the mobile user. It includesboth the cost of transmitting the headlight prefetchingsegments to the designated SAPs as well as the cost oftransmitting the requested segments to the mobile user. Tocharacterize the cost factors described above, we define theparameters listed in Table 1.

Since a prefetching assignment message for SAP i musttravel a distance of Di and a media segment to prefetch bySAP i must travel a distance of Hi, it is not hard to see thatCm and Cs can be characterized by the following equations:

Cm ¼ mXni¼1

Di; ð11Þ

Cs ¼ lXni¼1

ðSi �HiÞ; ð12Þ

Cp ¼ Cm þ Cs: ð13Þ

Furthermore, all segments retrieved by the local SAP mustbe downloaded to the mobile user for viewing. We,therefore, need the last mile cost as follows:


TABLE 1Parameters for Headlight Prefetching Cost Analysis

Cr ¼ dSr: ð14Þ

The total cost Ct of headlight prefetching and media servicecan, therefore, be formulated as

Ct ¼ Cp þ Cr: ð15Þ

With the cost factors formulated as above, a user cannow specify a desired maximal limit of cost Cupper which isused as the upper bound. In other words, besides qualityassurance, an equally important goal is to maintain theprocessing cost C such that

C < Cupper; ð16Þ

is satisfied at all time.

4.3 QoS-Aware Adaptation for HeadlightPrefetching

For a dynamic adaptation technique to work properly, weneed to determine exactly when to trigger the adaptation,what to adapt, and how to adjust it. In this section, wepresent the details of our QoS-aware dynamic adaptationtechniques.

4.3.1 Adaptation Timing and Checkpoints

When either the service quality drops below Qlower (thelower bound on quality), or the processing cost raises aboveCupper (the upper bound on cost), we must make somechanges to improve the situation. To combine the condi-tions into one, we define the triggering conditions fordynamic adaptation as follows:

Q < Qlower; ð17ÞC > Cupper; ð18ÞQ

C<Qlower

Cupper: ð19Þ

Equations (17) and (18) are the boundary conditions ofquality and cost, respectively. Equation (19) says that whenthe current quality level per unit cost is below the lowestquality allowed with highest permissible unit cost, then wereally need to do something to change the status quo.

With the successful identification of adjustment timing,we face a new problem though. Continuous monitoring ofthe quality and cost is still computationally intensive. Sincethe basic headlight prefetching covers the current cell prettywell, we choose to reevaluate the quality and cost ratiowhenever a user travels across a cell boundary. However, if auser slows down or even stops moving, then the prefetchedsegments for the current cell may soon be not enough tomaintain the desired level of quality. We, therefore, set up atimer for starting the reevaluation of quality and cost. Thevalue is set to be the projected time of stay of the user withinthe current cell. With the two adaptation checkpoints discussedabove, we can always keep up with the user despite thechanges in moving patterns.

4.3.2 Adaptation Targets

The second issue to deal with, on a successful adaptationdecision, is the targets to adjust and a good understandingof the effect of the adjustment on each target. Based on theheadlight prefetching model, we identify three main targets

for adjustment that have the most significant impact onprefetching effectiveness.

. Radius—This is the radius of the headlight zonewhich determines the lookahead extend of theprefetching. In general, faster moving users needlonger radii to have SAPs farther away prepared forservices.

. Angle—This is the span angle of the headlight zone,which determines the degree of concentration of theprefetching. Users that tend to wander around (i.e.,constantly changing directions) need larger angles tohave more surrounding SAPs ready for services.

. Intensity—This is the amount of virtual illuminanceincident on the headlight zone, which is used tocontrol the volume of prefetching. Basically, theusers with more steady patterns can be consideredfor prefetching with higher intensity.

4.3.3 Adaptation Strategies

After identifying adaptation timing and targets, we can nowdiscuss how to determine the proper adjustment. By theanalysis in previous sections, we can see that movingpatterns have significant impacts on prefetching decisions.Therefore, the first thing to do is to track down the usermovement patterns. Regression analysis provides us with arich set of methods for predicting the most likely track auser is about to take. In particular, we are interested in thecorrelation between speed and angle variations. Therefore,we define the following linear regression formula:

�0 ¼ �0 þ �1v0; ð20Þ

where �0 and v0 are the changes in moving angle and speed,respectively. Since the adaptation is triggered either by costraise or quality drop, the adaptation can, therefore, bedriven by the attempt to remedy the deficiency. Morespecifically, let Sr be the desired number of lookaheadsegments (say, 20 percent of media size) and p be the mediaplayback rate, then the new radius (r) and angle (�) can bedetermined as follows:

r ¼ Srp

vþ C �Qlower

Q� Cupperv0

� �; ð21Þ

� ¼ �þ�v0

jv0j j�0j: ð22Þ

Once the radius and angle are determined, the prefetchingSAPs and the exact number of segments to prefetch can bedetermined based on the previous intensity value. Then, areevaluation of the projected quality and cost can beconducted to see if the boundary conditions are satisfied.If not, the intensity can be adjusted such that both thequality and cost can be brought within bound.

Later in Section 7, we will show that simple adaptationstrategies proposed above can have significant impact onmedia-streaming performance, even with fast changingmoving patterns.

5 ADAPTIVE P2P MEDIA STREAMING

With the dynamic-chaining techniques proposed in [1], a useris chained to only one supplying partner at a time. To avoid


sudden disruption due to the departure of the supplyingpartner, belt sharing technique is introduced to maintain a setof potential supplying peers so that an alternative partner canbe found quickly if needed. To make the discussion self-contained, we briefly describe the techniques below.

The set of potential supplying peers is determined basedon two scores: preference score and tendency score. Let the mediarequesting user be P and the set of neighbors having part ofthe requested media be N ¼ fPi; i ¼ 1; 2; . . . ; ng. Assumingthat the speed vectors of P and Pi be V and Vi, the distancebetweenP andPi bedi > 0, and the angle betweenV andVi be�i. Also, let the number of neighbors already chained to Pi beni. Then, the preference score is defined as follows [1]:

Si ¼f � cos �i

di � ðni þ 1Þ �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðjV j � jVijÞ2 þ 1

q ; ð23Þ

where f is an adjustable scaling factor. Furthermore, let thepreference score and tendency score of P at time t be sðtÞand dðtÞ. Then, dðtÞ is defined as follows [1]:

dðtÞ ¼ 1 if dðt� 1Þ < 0 ^ sðtÞ > sðt� 1Þ;¼ dðt� 1Þ þ 1 if dðt� 1Þ > 0 ^ sðtÞ > sðt� 1Þ;¼ dðt� 1Þ if sðtÞ ¼ sðt� 1Þ;¼ dðt� 1Þ � 1 if dðt� 1Þ < 0 ^ sðtÞ < sðt� 1Þ;¼ �1 if dðt� 1Þ > 0 ^ sðtÞ < sðt� 1Þ:

ð24Þ

The neighbors are sorted first by preference score, then bytendency score. Whenever a partner search requirementarises, a node can simply try the users on the top of the listwith positive tendency scores rather than starting a newsearch.

Dynamic chaining and belt sharing have been shown to bequite effective as complementary techniques to work withheadlight prefetching in [1]. However, dynamic chaining andheadlight prefetching operate in distinctive modes. A user isserved either in chaining mode or in headlight prefetchingmode. Switching between the two modes usually result inundesirable interruption. Furthermore, relying on a singlesupplying partner is potentially fragile.

In this section, we improve the streaming efficiency aswell as robustness of dynamic chaining and belt sharingeven further in several ways.

. Eligible neighbors with high preference scores andpositive tendency scores are selected to form thesupplying partner set instead of relying on just a singlechaining peer.

. Multiple media segments are downloaded from allsupplying partners in the set instead of just one,which is potentially unstable.

. An adaptation technique is devised to dynamicallyadjust the size of the set to reflect the current statusof the supplying partners.

More specifically, the supplying partner set R of P isconstructed and dynamically maintained by the followingrules:

1. Initially, R ¼ fPij8j 2 N;Si � Sjg.2. If 9i; Si > Averagej2RðSjÞ, then R ¼ Rþ fPig.3. If jRj > jNj=2, then R ¼ R� fPig, where Pi 2 R ^8Pj 2 R;Si � Sj.

In other words, the set consists initially of the peers withthe highest preference score. Since the score changesdynamically, we add a peer into the supplying partner setwhenever its score is higher than the average score of thecurrent set. When the number of supplying partners exceedhalf of the number of neighbors, we remove the peer withthe lowest preference score.

After identifying the supplying partner set, the neededsegments are arranged to be downloaded from the peers inthe set in a round robin fashion. Naturally, jRj ¼ 0 indicatesthat we can’t find any qualified supplying peer. In suchcase, the required segments must be downloaded from theSAP using headlight prefetching.

6 SMOOTH INTEGRATION

As explained earlier, when no more supplying peer can befound, we must restart headlight prefetching for the user tomaintain the streaming service. Even with the dynamicheadlight prefetching and adaptive P2P streaming techni-ques, playback interruption may still occur during thetransition since it takes time for the requested segments to beprefetched. To overcome such an undesirable situation, wedevise a smooth integration technique to gracefully switchbetween headlight prefetching and P2P media streaming.The basic idea is to dynamically trigger and proportionallyincrease the prefetching degree when the stability of P2Pstreaming service decreases. More specifically, the P2P userlevel cooperation is always tried first for its lower servicecost, load balancing effect, and scalability. Then, a measureof the stability of the P2P streaming service is devised as anindicator of the likelihood of successful streaming from thesupplying peers. When the measure drops down to a levelthat is expected to hinder the seamless streaming service,preventive headlight prefetching is triggered. To avoidexcessive overhead, the degree of prefetching is proportion-ally adjusted with respect to the stability measure. In thisway, the SAP can immediately provide the needed segmentswhen no supplying peer can be located.

To obtain a good stability measure for the supplyingpartner set R, we reexamine the preference score as definedin (23). For each neighbor Pi, the highest possible value of Siis reached when �i ¼ 0; ni ¼ 0, and V ¼ Vi. In such case,Si ¼ f=di. Then, a measure of stability of R can be definedas the fraction of current total score against the highestpossible total score. That is,

B ¼P

Pi2R SiPPi2R

fdi

: ð25Þ

The degree of prefetching to perform can simply be definedas follows:

D ¼ 1�B: ð26Þ

In other words, when the stability drops from 1 toward 0,we proportionally increase the prefetching from 0 toward 1.

One problem with the initial definition above is that weneed to start prefetching whenever the stability measure isnot perfect. This can potentially lead to excessive overhead.One way to remedy the problem is to trigger prefetchingonly when B is lower than a threshold such as 1=2.


In general, a peer with a high enough preference score isgood enough to be considered as a stable peer. We can,therefore, define the stability measure for each individualpeer Pi to be

Bi ¼Sifdi

: ð27Þ

Then, a peer with stability measure higher than a threshold� (say, 60 percent) can be considered a stable peer. Anotherstability measure for the supplying partner set R can thenbe defined as the fraction of stable peers among allneighbors. That is,

B ¼ CountPi2RðBi � �ÞjNj : ð28Þ

The degree of prefetching can be determined in the sameway as (26).

With well-defined prefetching degree and stabilitymeasure, we can now smoothly integrate dynamic headlightprefetching and P2P media streaming with graceful transi-tion between the two as illustrated in Fig. 2. Whenever amedia request is received from a mobile user, the P2Pstreaming service is always tried first, as explained earlier. Ifthe supplying partner set can be successfully formed, weproceed with P2P media streaming. Whenever the stabilityof P2P streaming service drops below a threshold (1/2 in ourexperimentations), the proportional headlight prefetching istriggered. Prefetching takes over when no more P2P serviceis available. Then, the QoS and cost of prefetching arecarefully monitored to dynamically adjust the headlightmodel whenever necessary. During the prefetching service,

the opportunity for P2P streaming is continuously mon-itored. Whenever the supplying partner set can be formed,the lower cost P2P streaming is preferred.

7 SIMULATION AND PERFORMANCE EVALUATION

To evaluate the performance of the proposed dynamicadaptation techniques, we use the Java-based simulationenvironment developed in [1] with parameters and valueranges as listed in Table 2. Our primary goal is to comparethe performance of dynamic-adaptation techniques againstheadlight prefetching and single supplier chaining asdiscussed in [1]. In particular, we vary the speed changingand direction changing probabilities to understand howwell the proposed techniques handle fast changing andunstable moving patterns.

7.1 Evaluation of Dynamic Headlight Prefetching

In the first set of experiments, we compare the average QoSand processing cost as defined in Sections 4.1 and 4.2. FromFigs. 3 and 4, we can see that the cost reduction and QoSimprovement of dynamic adaptation techniques over staticprefetching are both quite evident. When integrated withheadlight shifting and sharing, we can even obtain a costreduction of 42 percent.

Moving speed is known to have significant impact onservice quality. We vary the speed from 10 to 50 m/s andcompare the performance between static and dynamicprefetchings. From Fig. 5, it is clear that the number ofinterruption increases rapidly with high moving speed onstatic prefetching. Dynamic prefetching provides marginalimprovement when the moving speed is low. This is to be


Fig. 2. Smooth integration of dynamic headlight prefetching and P2P media streaming.

expected since the situation offers little chance for theadaptation techniques to bring into full play. However, asthe moving speed increases, dynamic prefetching cansignificantly decrease the number of interruption, whichclearly demonstrates the power of the adaptation techniques.

The superiority of dynamic prefetching is even moreevident on average segment download time as depicted inFig. 6. On static prefetching, we observe a sudden increasestarting from 40 m/s. With dynamic adaptation, however,we can successfully reduce the download time to areasonable level. Fig. 7 is a closer look at the performanceof dynamic headlight prefetching. It can be seen that theproposed adaptation techniques improve the performanceof both headlight shifting and sharing.

To evaluate the responsiveness of the dynamic adaptationtechniques, we increase the speed changing probability andmeasure its impact on QoS and cost. From Fig. 8, we can

observe that, starting from a low QoS level, dynamicadaptation can quickly improve the quality level to climbover the lower bound and continue to improve over time,even under high probability of change. From Fig. 9, we can


TABLE 2Simulation Parameters and Settings

Fig. 3. Cost reduction with dynamic adaptation.

Fig. 4. QoS improvement with dynamic adaptation.

Fig. 5. The effect of moving speed on playback interruption (staticversus dynamic headlight prefetching).

Fig. 6. The effect of moving speed on average segment download time(static versus dynamic headlight prefetching).

see that the proposed techniques can also bring down the cost

continuously over time under changing probability of

moving speed.In a similar set of experiments, we evaluate the respon-

siveness of our techniques by varying the probabilities of

direction change. Fig. 10 demonstrates that the QoS levelimproves quickly over time. Likewise, Fig. 11 shows that,even under high direction changing probability, our techni-ques can still successfully bring down the average cost.

The two sets of experiments demonstrate that ouradaptation techniques are highly responsive in adjustingthe prefetching services to cope with fast changing userbehavior.

To understand the relative impact of speed and directionchanging patterns on adaptation, we cross evaluate bothfactors on playback interruption and QoS. Fig. 12 shows that,while dynamic adaptation can take advantages of morestable patterns to lower the number of playback interrup-tions, speed and direction changing have comparable effects.

When it comes to QoS, however, the story is clearly notthe same. Fig. 13 demonstrates that the proposed techniquesare more sensitive to direction change than speed variation.This is conceivable since direction changes are more likelyto lead the user toward areas with little or even noprefetched segments available which results in the drop-ping of QoS level.

As a summary, the proposed dynamic adaptation techni-ques live up to our expectations on swift response to fast


Fig. 7. The effect of moving speed on average segment download timewith dynamic headlight prefetching (closer view).

Fig. 8. The effect of speed changing probability on QoS.

Fig. 9. The effect of speed changing probability on cost.

Fig. 10. The effect of direction changing probability on QoS.

Fig. 11. The effect of direction changing probability on cost.

changing moving patterns as well as continuous improve-ment of QoS/cost ratio. They are invaluable enhancement tothe headlight prefetching techniques to support much betterstreaming services in mobile environments.

7.2 Evaluation of Adaptive P2P Streaming andIntegrated Media Service

In this section, we evaluate the adaptive P2P streamingtechniques with various combinations of dynamic headlightprefetching strategies. The performance results are com-pared with static headlight prefetching and chaining withbelt sharing technique.

Fig. 14 demonstrates the effect of moving speed onaverage download time while Fig. 15 depicts the results ofsimilar experimental settings on playback interruption. Fromboth figures, we can observe that adaptive P2P streamingsuccessfully improve the performance of previous approach,especially when integrated with dynamic headlight pre-fetching and the shifting/sharing optimizations.

Figs. 16 and 17 are the performance results of similarcombinations of strategies with direction changingprobability ranging from 50 to 80 percent. The improvementis equally impressive. It is interesting to observe that thecombination of adaptive P2P streaming and dynamic

prefetching with shifting and sharing significantly outper-

forms other strategies. These results demonstrate that the


Fig. 14. The effect of speed on average download time with variouscombinations of strategies.

Fig. 15. The effect of speed on playback interruption with variouscombinations of strategies.

Fig. 12. The effect of speed and direction changing probabilities onplayback interruption.

Fig. 13. The effect of speed and direction changing probabilities on QoS.

Fig. 16. The effect of direction changing probability on averagedownload time with various combinations of strategies.

integrated media service successfully improves the perfor-mance of static techniques.

In the next set of experiments, we are to understand howfast can our integrated adaptation techniques respond todynamically changing moving patterns, especially on themaintenance of good quality and cost levels. As thesimulation proceeds, we set the speed changing probabilityto 10 percent, raise it to 80 percent, and then down to50 percent. Fig. 18 shows that it is relatively easy tomaintain QoS above the lower bound when the speedchanging probability is low. When the probability suddenlyincreases to 80 percent, the QoS drops to an unacceptablelevel, as expected. However, our adaptation techniques canquickly react to the change and raise the QoS back todesirable level. When the probability drops back to50 percent, the QoS is continuously maintained to be abovethe lower bound.

In a similar setting, we demonstrate the adaptability of ourtechniques in maintaining much lower service cost than theupper bound most of the time as depicted in Fig. 19. Evenwhen a sudden change in moving pattern, which results in

higher service cost momentarily, our techniques can swiftlybring down the cost by adapting the service strategies.

The set of experiments on the responsiveness of theintegrated service demonstrates that, when facing with thesituation where most of the mobile users are traveling withunstable patterns, our techniques can still maintain goodlevels of QoS and service cost on media streaming.

8 CONCLUSIONS AND FUTURE WORK

In this paper, we have presented QoS-based dynamicadaptation techniques to significantly improve the respon-siveness of two-level cooperative streaming service withheadlight prefetching and P2P chaining. In the face of fastchanging moving patterns, the techniques achieve theireffectiveness by continuous monitoring of QoS and proces-sing cost, then adjust the streaming service strategiesaccordingly. Furthermore, smooth integration of the twolevels of cooperation provides even higher quality ofservices to mobile users at lower cost.

Even with effective adaptation techniques, undesirableplayback interruption can still occur since all the proposedtechniques are passive in the sense that all strategies can onlybe applied after receiving the media requests from the users.No matter how well we manage our prefetching and P2Pstreaming, the instability of wireless communication and theunpredictability of user movement can still lead to discon-nection and, therefore, playback interruption or long down-load time. We are currently investigating a bidirectional pushand pull technique to actively disseminate informationtoward their most likely consumers as well as forwardrequests toward close by neighbors that are most likely topossess the needed answers. Working together with clientcaching, it is possible to significantly improve service quality.

Another possible remedy of the problem is to developpersonalized data service solution based on the useractivities. Using behavior-mining techniques, it is possibleto identify user activities and predict the next location and/or service invocation. In such case, we can provideproactive services which are expected to significantlyreduce the likelihood of playback interruption.


Fig. 18. The performance of integrated streaming service on QoS withdynamic speed changing probability.

Fig. 17. The effect of direction changing probability on playbackinterruption with various combinations of strategies.

Fig. 19. The performance of integrated streaming service on cost with

dynamic speed changing probability.

In this paper, we have assumed that a media object is

simply a sequence of media segments. The streaming rate is

set to be enough to keep up with normal playback rate. It is

potentially beneficial to look into media characteristics such

as encoding or even content for better services. Separate

treatment of prerecorded and live media streaming can also

result in better strategies. We plan to extend the current

techniques to explore media features as well as the

possibility of variable bit rate delivery.

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Shiow-yang Wu received the BS and MSdegrees in computer engineering from NationalChiao Tung University, Hsinchu, Taiwan, ROC,and the PhD degree in computer science from theUniversity of Texas at Austin in 1984, 1986, and1995, respectively. He is an associate professorof the Department of Computer Science andInformation Engineering, National Dong HwaUniversity, Hualien, Taiwan, ROC. His researchinterests include data/knowledge bases, mobile/

pervasive computing, RFID data management middleware, and perso-nalized information services. He is a member of the IEEE and the IEEEComputer Society.

Cheng-en He received the BS degree incomputer science and information engineeringfrom SooChow University, Taiwan, ROC, andthe MS degree in computer science fromNational Dong Hwa University, Taiwan, ROC,in 2006 and 2008, respectively. His researchinterests include mobile media streaming andpeer-to-peer network.

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QoS-Aware Dynamic Adaptation for Cooperative Media Streaming in Mobile Environments