Embed Size (px)
Citation preview
Measurement 44 (2011) 1661–1668
Contents lists available at ScienceDirect
Measurement
journal homepage: www.elsevier .com/ locate/measurement
Experimental assessment of the effects of cross-traffic on Wi-Fivideo streaming
Leopoldo Angrisani a,⇑, Konstantinos G. Kyriakopoulos b, Aniello Napolitano c, David J. Parish b,Michele Vadursi d, William G. Whittow b
a Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli Federico II, Via Claudio, 21, 80125 Napoli, Italyb Department of Electronic and Electrical Engineering, Loughborough University Loughborough, LE11 3TU, UKc SESM s.c.a.r.l., Via Circumvallazione Esterna, 116, 80014 Giugliano in Campania (Napoli), Italyd Dipartimento per le Tecnologie, Università degli Studi di Napoli ‘‘Parthenope’’, Centro Direzionale di Napoli, Isola C4, 80143 Napoli, Italy
a r t i c l e i n f o
Article history:Received 8 October 2010Received in revised form 30 March 2011Accepted 21 June 2011Available online 30 June 2011
Keywords:Wireless networks test and measurementMeasurements for networkingWi-FiVideo streamingCross-layer measurements
0263-2241/$ - see front matter � 2011 Elsevier Ltddoi:10.1016/j.measurement.2011.06.012
⇑ Corresponding author. Tel./fax: +39 081768317E-mail addresses: [email protected] (L. Angrisa
(K.G. Kyriakopoulos), [email protected] (A. Napolboro.ac.uk (D.J. Parish), michele.vadursi@[email protected] (W.G. Whittow).
a b s t r a c t
Wi-Fi networks are the first and sometimes only choice for the video streaming in homes,airports, malls, public areas and museums. However, Wi-Fi networks are vulnerable tointerference, noise and have bandwidth limitations. Due to the intrinsic vulnerability ofthe communication channel, and the large number of variables involved, simulation aloneis not enough in the evaluation of the performance of wireless networks. Actually, there is atendency to give experimental tests a central role in the assessment of Wi-Fi networks per-formance.
The paper presents an experimental analysis of the effects of cross traffic on the perfor-mance of video streaming over Wi-Fi, based on cross-layer measurements. Experiments arecarried out in a semi-anechoic chamber, to prevent the results from being influenced byexternal factors. The experimental results permit to analyze the influence of cross trafficcharacteristics on cross layer measures and objective video quality metrics evaluatedthrough a standardized approach.
� 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Multimedia sharing, thanks to the progress of the videostreaming technology underlying it, is rapidly spreading asan everyday practice. Nowadays, people want to access re-motely stored videos from every part of their homes, ontheir own laptop or pocket PC. Access to multimedia con-tents via a PDA, a laptop or a pocket PC in airports, malls,public areas and museums is becoming more and morewidespread, for entertainment purposes, public utilityinformation, and ubiquitous commercial communication.
. All rights reserved.
0.ni), [email protected]), [email protected] (M. Vadursi),
While being the first choice in home connectivity,thanks to their flexibility, low cost and quickness of instal-lation, Wi-Fi networks, that are wireless local area net-works (WLANs) based on IEEE 802.11 specifications, arethe most practical technology solution for video streamingin public areas.
Despite such a huge development, the performance ofWi-Fi networks is sometimes hard to predict and to guar-antee. This is mainly due to the poor stability and reliabil-ity of the radio link. In fact, while on wired channels signalintegrity is assured by mechanical, electrical and protocolcharacteristics of the physical and data link layers [27],on wireless channels unpredictable and uncontrollableinterference can severely affect data transmission, and ulti-mately degrade or even compromise the desired perfor-mance of the network [1]. The Wi-Fi standard exploits ascarce, shared, and noisy spectrum, i.e. the unlicensed
1662 L. Angrisani et al. / Measurement 44 (2011) 1661–1668
2.4 GHz Industrial Scientific Medical (ISM) band, on whichother devices may operate simultaneously [2–6].
Performance evaluation based on simulations can be ofhelp, but is not sufficient, due to the great number of vari-ables involved. In such a direction, useful information canbe achieved through ad hoc laboratory and on-field mea-surements, exploiting proper test beds [7,8]. In the recentpast, cross-layer measurements have come out to be apowerful option to assess and predict the performance ofwireless and hybrid networks, as well as to troubleshootthem [9–16]. In the literature, a number of papers investi-gate on the feasibility of video streaming over Wi-Fi net-works. In many cases, efficient solutions are proposed forimproving the quality of video streaming. Nevertheless,only few of such contributions face the problem from anexperimental point of view [15,16]. Experimental perfor-mance assessment is, indeed, very important under criticalconditions, e.g. when noise, in-channel interference and/orcross traffic are present. In such cases, in fact, the complex-ity of the protocol makes it difficult to obtain reliableanalytical and simulative results.
Extensive standardization related work has been under-taken on experimental measurement approaches foractivities conducted at different layers of the networkabstraction, however, these tend to be concentrated on afew layers only. For example, work has been undertakento assess the quality of a video sequence at replay (e.g.Mean Opinion Score [29]; VQM [26]), this is essentiallyan Application Layer issue. Separate standards relate tonetwork performance measurement at the middle layers[30]; yet other independent approaches consider RF mea-surement at the physical layer [31]. However, to the bestof the author’s knowledge, little work has considered thesimultaneous measurement of multiple performance met-rics at different layers of the network. Hence this paperpresents an approach to the measurement of the effectsof interference at different layers on a Wireless networksupporting video transfer for which no single coherentmeasurement standard applies.
Regarding Wi-Fi video streaming quality, a related workwas presented in Ref. [21], where the video performanceover WLAN is experimentally assessed with a cross-layerapproach. The paper is interesting and investigates the im-pact of distance, possible obstacles and motion on the vi-deo quality, considering different metrics, including thepeak signal-to-noise ratio (PSNR), which is used as a qual-ity indicator. It has to be noticed, however, that the choiceof transmitting the cross traffic from the same source andto the same destination as with the video stream can raisesome methodological concerns. In fact, more commonlybandwidth limitations are due to other hosts on the samewireless network rather than the same host, and the artifi-cial cross traffic for the experiments should compete forthe same wireless link, but should not at the same timerepresent an overhead for the host receiving the video,otherwise it could slow it down, and alter the results.
The authors have recently investigated the effects ofGaussian noise on the streaming time of the TCP (Transmis-sion Control Protocol) connection [19]. However, UDP (UserDatagram Protocol) is by far more adapt and widespread forwireless video streaming, due to its connectionless features.
Thus, in this paper the attention is focused on the experi-mental analysis of Wi-Fi video streaming quality overUDP, with regard to both a normal UDP connection (herein-after, referred to as normal mode) and a quality-of-service-oriented one (QoS mode). In particular, the goal is to evaluatetheir performance in the presence of cross traffic. In fact,while the performance of Wi-Fi video streaming over UDPhas already been studied with regard to noise and interfer-ence [17,18], an experimental study based on cross-layermeasurements, including VQM (Video Quality Metric)[26], in presence of cross traffic, is not reported in theliterature.
The paper is organized as follows: an overview of theIEEE 802.11x standards is given in Section 2; the measure-ment test-bed, tools and procedure are respectively pre-sented in Sections 3–5; experimental results are shownand discussed in Section 6; finally, conclusions are givenin Section 7.
2. IEEE 802.11x standards
The family of IEEE 802.11 standards concerns wirelessconnectivity for fixed, portable, and moving stations with-in a local area. It applies at the lowest two layers of theOpen System Interconnection (OSI) protocol stack, namelythe physical layer and data link layer.
The physical layer (PHY) essentially provides threefunctions. First, it interfaces the upper MAC sub-layer fortransmission and reception of data. Second, it provides sig-nal modulation through direct sequence spread spectrum(DSSS) techniques or orthogonal frequency division multi-plexing (OFDM) schemes. Third, it sends a carrier senseindication back to the upper MAC sub-layer, to verify activ-ity in the wireless channel. The data link layer includes theMAC sub-layer, which allows the reliable transmission ofdata from the upper layers over the PHY media. To thisaim, it provides for a general controlled access to theshared wireless media, called carrier-sense multiple accesswith collision avoidance (CSMA/CA). It also protects thedata being delivered through proper security policies. TheIEEE 802.11 family currently includes multiple extensionsto the original standard, which are based on the same basicprotocol and are essentially different in terms of modula-tion techniques. The most popular extensions are those de-fined by the IEEE 802.11a/b/g amendments (also referredto as standards), on which most of today’s manufactureddevices are based. A further extension, namely IEEE802.11e standard [20], has been emanated to support qual-ity of service (QoS) in wireless environment. It defines spe-cific MAC layer strategies to assure reliable performance onthe wireless link for different traffic categories.
Nowadays, the IEEE 802.11g standard is the most widelyaccepted worldwide. It involves the license-free 2.4 GHzISM band (2.4–2.4845 GHz), in the same way as the IEEE802.11b standard, and supports a maximum data rate of54 Mbps, in the same way as the IEEE 802.11a standard.IEEE 802.11g standard devices are backwards compatiblewith IEEE 802.11b ones. They use the OFDM modulationscheme for data rates of 6, 9, 12, 18, 24, 36, 48, and 54 Mbps,and revert to complementary code keying (CCK, as in the
L. Angrisani et al. / Measurement 44 (2011) 1661–1668 1663
case of the IEEE 802.11b standard) for 5.5 and 11 Mbps, anddifferential binary phase shift keying (DBPSK)/differentialquadrature phase shift keying (DQPSK)+DSSS for 1 and 2Mbps. Moreover, 14 different frequency channels are de-fined, each of which characterized by 22 MHz bandwidth.In USA, channels 1 through 11 are allowed, in Europe chan-nels 1 through 13 can be used, and in Japan only channel 14is accessible. Due to the available bandwidth, the channelsare partially overlapped, and the number of non-overlap-ping usable channels is only 3 in USA and Europe (e.g.,channels 1, 6, and 11).
To assure QoS, the IEEE 802.11g standard refers to IEEE802.11e one. With respect to the distributed coordinationfunction (DCF), in fact, the IEEE 802.11e standard includesan additional hybrid coordination function (HCF), whichboth combines the capabilities of DCF and of the point coor-dination function (PCF) and adds some improvements. TheHCF uses both a contention-based channel access method,called enhancement distributed channel access (EDCA)mechanism, for contention-based transfer, and a controlledchannel access, referred to as controlled channel access(HCCA) mechanism, for contention-free transfer. In bothmechanisms (EDCA and HCCA), the station is allowed totransmit only when it gains a transmission opportunity(TXOP), called EDCA TXOP or HCCA TXOP, respectively. Inthe EDCA mechanism, QoS is performed through the use ofaccess categories (ACs), characterized by traffic categories(TC) and multiple independent backoff entities. In the IEEE802.11e standard, a station is characterized by four ACs,having independent transmission queues. An AC is basicallyan enhanced variant of the DCF, which contends for a TXOPaccording to some suitable parameters [20].
In the HCCA mechanism, the access to the wireless med-ium is managed by using a hybrid coordinator (HC), whoseaccess priority is higher than that of a station supportingQoS. The HC gains the control of the wireless medium wait-ing for a shorter time interval with respect to the stationsusing the EDCA procedures. In particular, when the wirelessmedium is assessed idle for at least one point inter-frame
Fig. 1. Measureme
space (PIFS), it transmits the first frame with such a durationvalue as to cover the contention-free period. During this per-iod, the HC assigns to the stations the needed TXOPs. At theend of either mechanism, an admission control phase is per-formed, as described in [20].
3. Measurement test-bed
Tests are conducted within a protected and controlledenvironment, i.e. a shielded semi-anechoic chamber com-pliant with electromagnetic compatibility requirementsfor radiated emission tests. Experiments aim to emulatethe actual operating scenario of a WLAN compliant withIEEE 802.11g standard. A cross-layer approach is appliedin order to assess the performance of WLAN in supportingvideo streaming applications. More specifically, differentmetrics at different protocol stack layers are measured:streaming time at application layer, lost packets at trans-port layer, and MAC retransmission at link layer. Addition-ally, video quality is measured through the software VQM,which implements a standardized method to objectivelymeasure video quality [26].
A block diagram of the measurement test-bed is shownin Fig. 1. It consists of the following components:
(1) A WAP54G access point (AP) by Linksys, compliantwith the IEEE 802.11g standard.
(2) A notebook ‘‘Host1’’, with Intel Pentium Dual Core @1.73 GHz, 4 GB of RAM, which communicates withthe AP at a 100 Mbps rate and through a 5 m lengthUTP category 3 cable, and acts as the source of thevideo stream.
(3) Two notebooks, ‘‘Host2’’ and ‘‘Host3’’, which areequipped with Intel Core 2 Duo @ 2 GHz processor, 4 GBRAM and built-in Wi-Fi card (Airport) compliant with IEEE802.11b/g standard, and act, respectively, as the receiver ofthe video stream and as the source of the cross traffic. Theyare both connected to the AP through an IEEE 802.11gwireless connection, according to a DCF MAC layer access
nt testbed.
1664 L. Angrisani et al. / Measurement 44 (2011) 1661–1668
method along with CSMA/CA protocol. The parameters ofthe hosts are provided for the sake of completeness ofthe experimental setup description and for potentialexperiment repeatability purposes.
(4) Two notebooks, ‘‘Host4’’ and ‘‘Host5’’, which areequipped with Intel Core 2 Duo @ 2 GHz processor and4 GB RAM. Both notebooks have their built-in Wi-Fi carddisabled, and are connected to an ASUS USB adapter Wi-Fi card, namely WL-167G, compliant with IEEE 802.11gstandard. They are thus used as monitoring devices forHost2 and Host3.
(5) A further notebook, ‘‘Host6’’, which is connected toan Ethernet switch to which also Host2, Host3, Host4 andHost5 are connected. This makes it possible to control viaremote desktop the four hosts in the chamber from Host6,which is outside.
(6) Another ASUS WL-167G, which is placed near the APconnected to Host6 through a USB cable.
The same test-bed characterizes both the operatingmodes that have been considered for the experiments.DCF and HCF access methods are respectively exploitedin the normal and QoS mode. Indeed, in the QoS mode,the AP is configured in such a way to recognize the videostreaming packets as a traffic category, and applies thespecified QoS, whereas, in the normal mode the best-effortservice is invoked.
4. Measurement tools
The whole set of software tools used in the experimentsare open-source, free available in the public domain. Spe-cifically, VideoLAN [24] is a free cross-platform mediaplayer released under the GNU General Public License[28]. It can be used as a multicast and unicast streaminggenerator, supporting a large number of audio and videoformats. It also allows the choice of the receiver buffer timelength. In the experiments, VideoLAN is used by Host1 togenerate the test videos, and by Host2 to decode the re-ceived data flow with a buffer time length of 300 ms.
Wireshark is an open-source packet analyser tool formultilevel packet analysis [22]. It allows in-depth investi-gation about network problems and performance, andaccurate testing of new protocols. It provides meaningfulinformation about the incoming packets characteristicsand contents. All information carried by packets is read,analyzed and presented to the user by Wireshark. This in-clude fields like timestamps, which indicate the time whena packet was recorded and flags in the packet indicatingwhether a packet has been retransmitted at the MAC layeror not. Packet losses are calculated by counting with Wire-shark the number of packets sent by the sender and thenumber of packets received at the receiver. The numberof lost packets can be calculated by subtracting the numberof packets received from the number of packets sent. In theexperiments, it is used both to assess the correct operationof the WLAN and measure the PLR.
D-ITG is a distributed Internet traffic generator [27],whose architecture allows the generation of traffic andthe regulation of key parameters such as packet inter-
departure time and length. It also allows measuring severalQoS parameters at both sender and receiver side, andobtaining a complete report of measured parameters overthe whole measurement time. D-ITG is, in particular, usedto generate WLAN traffic in the second scenario.
VQM is a Video Quality Metric algorithm, based on themodels referred to by ITU Recommendation BT.1683 [26].It provides video quality estimates rather close to thoseachievable from subjective analysis. It requires two inputvideo streams: the original one, taken as reference, andthe effectively displayed one, possibly corrupted, to beanalyzed. As final result, VQM provides an overall qualityscore, mapped on a scale from 0 up to 1, where 0 meansthat no impairment is perceivable and 1 that a maximumlevel of impairment is visible.
5. Measurement procedure
Experiments are conducted emulating a real-world uni-directional video streaming, compliant with H.264 [25],and characterized by different video data rate. The featuresof the video used in the tests are chosen according to a typ-ical configuration exploited for sharing and spreading vi-deo contents over the Internet. In particular, the video ischaracterized by, (i) Constrained Baseline profile (CBP),(ii) level two, (iii) CIF (Common Intermediate Format,352 � 288) picture format, (iv) three different mean datarate respectively of nearly 300 kbit/s (referred to as ‘‘low’’profile), 600 kbit/s (referred as ‘‘medium’’ profile) and800 kbit/s (referred as ‘‘high’’ profile), and (v) RTP/UDP ascommunication protocol.
Concurrently to the video streaming, a suitable crosstraffic is generated on the same wireless link, in bothmodes, by synthetic traffic generator D-ITG. The cross traf-fic profile is CBR (constant bit rate), with bit rates equaleither to 20 Mbps or 40 Mbps in order to encompass differ-ent conditions of network load. Furthermore, different IPcross traffic packet size is chosen, respectively equal to512 bytes and 1400 bytes. Experiments with no cross traf-fic are also carried out.
Generally, Host2 generates a request to Host1, throughthe AP by wireless connection, for streaming an H.264 en-coded video located on Host1. Once the connection is ac-cepted, Host1 sends the video to Host2 via the AP,exploiting a UDP connection. Host2 downstreams the re-ceived H.264 video using the VLC media player.
The sniffer software Wireshark is installed on Host4,Host5 and Host6 in order to process the data captured byWL167G cards. The choice of having monitoring hostswhich are different from those involved with the WLANpermits to gather packets retransmissions at MAC layer.Otherwise, they could not be captured from the machinethat is generating the retransmissions neither from themachine that the retransmissions are destined to.
A cross-layer approach is applied in order to assess theperformance of WLAN in supporting video streamingapplications in presence/absence of QoS mechanisms.More specifically, different metrics at different protocolstack layers are measured, such as streaming time at appli-cation layer, lost packets at transport layer, and MAC
Fig. 2. PLR results in the normal mode scenario.
L. Angrisani et al. / Measurement 44 (2011) 1661–1668 1665
retransmission at link layer. The streaming time is mea-sured from the log file of the client computer as the differ-ence of the timestamp of the last UDP video packet fromthe timestamp of the first UDP video packet. The numberof MAC retransmissions at link layer and the packet loss ra-tio at transport layer are jointly measured, because theygive different information: the former gives an indicationof the relative data link layer conditions and can be signif-icantly different from zero even when no packet is lost attransport layer. Additionally, video quality is measuredthrough the software VQM, which implements a standard-ized method to objectively measure video quality in off-line mode. In detail, the streamed video is stored by VLCon the client after each experiment, and then comparedby VQM to the original video file, which acts as reference.
Fig. 3. Retransmissions results in the normal mode scenario.
6. Experimental results
6.1. Normal mode scenario
Table 1 synthesizes the results of 15 experiments re-lated to a normal mode UDP connection. Experiments arefirst ordered by cross traffic bit rate, then by cross trafficpacket size and, finally, by video bit rate. Figs. 2–5 respec-tively depict packet loss ratio (PLR), number of MAC-layerretransmissions (Retx), time to stream and VQM results.
The first three experiments show that when the videostreaming quality (VQM) is almost perfect no cross trafficis present, as none of the packets is lost. Although thesetests could seem banal, it has been done to verify thatthe connection is working properly, as so is the application.
There is a clear influence of cross traffic rate on the per-formance of the video streaming and a direct relation be-tween PLR and cross traffic bit rate. Fig. 2 clearly showsthis trend: the higher the cross traffic rate, the higher (onaverage) the number of packets that do not reach the recei-ver due to congestion. Given that the PLR is the ratio ofpackets received over packets transmitted, the PLR in-creases with increasing cross traffic rate. However, con-trary to what one would reasonably expect, anothermetric that is the number of retransmissions (see Fig. 3),seem to prove such consideration wrong. The experimental
Table 1Measurement results in the normal mode scenario.
# Cross traffic Video profile Measureme
Packet size (byte) Bit rate (Mbit/s) Packets sen
1 0 0 Low 52762 0 0 Medium 61923 0 0 High 66174 1400 20 Low 52375 1400 20 Medium 60476 1400 20 High 65197 512 20 Low 41528 512 20 Medium 60349 512 20 High 6468
10 1400 40 Low 510511 1400 40 Medium 602712 1400 40 High 645913 512 40 Low 534714 512 40 Medium 614415 512 40 High 6412
outcomes show that number of retransmission is almosthalved when the bit rate doubles from 20 Mbit/s to40 Mbit/s. In fact, looking at the number of retransmissionswhile forgetting about the PLR is misleading, but thanks tothe cross-layer approach such a paradox can be explainedas follows. When the wireless link becomes more con-gested, more video packets are lost because they aredropped by the AP. As retransmissions are counted atMAC layer, their number decreases because less videopackets are transmitted on the wireless link. The depen-dence of PLR on cross traffic packet size can also be simply
nt results
t Retransmissions PLR (%) Time to stream (s) VQM score
1 0 116.7 05 0 112.9 05 0 135.1 03666 0 157.1 0.14033 0 114.2 0.13817 0 155.5 0.12354 21.8 112.8 0.43503 20.4 128.2 0.43825 20.3 141.3 0.42111 28.5 152.4 0.62131 27.6 140.5 0.62309 26.9 164.5 0.61367 55.2 137.2 0.81272 50.3 119.2 0.81306 50.9 143.8 0.8
Fig. 4. Streaming time results in the normal mode scenario.
Fig. 5. VQM results in the normal mode scenario.
1666 L. Angrisani et al. / Measurement 44 (2011) 1661–1668
explained. Given the cross traffic bit rate, smaller packetsmean a higher number of total packets and therefore,according to the MAC layer protocol, a higher overheadwhich causes the channel to be occupied for a larger pro-portion of time. Thus, more video packets are dropped inthe queue of the AP and, consequently, lost. Therefore, withsmaller cross traffic packet size, the PLR increases.
Finally, it is interesting to observe that while the PLR isextremely sensitive to cross traffic, the streaming timeseems to vary randomly but without significant correlation
Table 2Measurement results in the QoS mode scenario.
# Cross traffic Video profile Measureme
Packet size (byte) Bit rate (Mbit/s) Packets sen
1 0 0 Low 52762 0 0 Medium 61923 0 0 High 66174 1400 20 Low 52375 1400 20 Medium 60476 1400 20 High 65197 512 20 Low 41528 512 20 Medium 60349 512 20 High 6468
10 1400 40 Low 510511 1400 40 Medium 602712 1400 40 High 645913 512 40 Low 534714 512 40 Medium 614415 512 40 High 6412
to it. Such a behavior is peculiar to UDP: the video sourcehost emits packets at the given rate, then these packetshave to share the channel with cross traffic and a numberof them is possibly dropped by the AP. However, being UDPa connectionless protocol, none of these packets is retrans-mitted at transport layer and, so, the streaming time doesnot vary significantly. On the contrary, it is the quality ofthe video that is affected and reflects the packet loss. HadTCP been used, a quick degradation of the streaming timewould have been observed as the cross traffic increased,as already noticed with noise [19].
It is interesting to observe that, in line of principle, arelation between the cross traffic and video quality couldbe exploited by the network for extracting informationabout video quality, on the basis of the actual amount ofdata traffic present in the network. A proper control strat-egy of data traffic flow could thus be implemented to as-sure a defined quality to video steaming applications.Similarly, the relation between VQM and PLR can allowthe video receiver to implement a suitable feedback proce-dure towards the AP for reducing the data traffic into thenetwork as long as the desired video quality is achieved.
6.2. QoS mode scenario
As already discussed, in the QoS mode scenario, theWLAN has suitable been configured for providing QoS bygiving precedence to video streaming packets with respectto cross traffic packets. More specifically, the AP has beenconfigured in such a way as to classify the video streamingpackets as a traffic category (i.e. AC_VI), through properrules, and supply the QoS according with the followingparameters: (i) TXOP equal to 3008 ms; CWmin = 15; andCWmax = 31. Being these values suggested by IEEE802.11e standard for AC_VI category, they are also givenas default ones into any AP setup configuration.
The obtained results, in terms of PLR, VQM score, timeto stream and retransmissions, are summarized in Table2 for all the experiments. Figs. 6–9 separately show themetrics taken into consideration.
From the measurement results, the following consider-ations can be made.
nt results
t Retransmissions PLR (%) Time to stream (s) VQM score
1 0 116.7 02 0 112.9 01 0 114.1 0100 0 120.1 0.1107 0 144.2 0.1108 0 143.5 0.189 0 138.6 078 1.0 146.2 0.2120 1.2 141.3 0.175 0 142.0 0.1111 0.6 138.9 0.1160 1.0 141.6 0.2101 0 129.6 0131 1.3 135.2 0.1260 4.0 147.2 0.4
Fig. 6. VQM results in the QoS mode scenario.
Fig. 7. PLR results in the QoS mode scenario.
Fig. 8. Retransmission results in the QoS mode scenario.
Fig. 9. Time to stream results in the QoS mode scenario.
L. Angrisani et al. / Measurement 44 (2011) 1661–1668 1667
� VQM score is strictly related to PLR. Indeed, only when aheavy PLR is experienced the video quality worsens. This isthe case of experiment number 15. On the contrary, if lowPLR values are measured, the final quality falls into anacceptable value range.
� There is a non linear relationship between cross traffic(both rate and packet size) and PLR as seen in Fig. 7. Ini-tially, PLR remains very small (PLR 6 1.3%) as the crosstraffic is not high enough to create congestion in thelink and, therefore, there are no lost packets. However,if a threshold is exceeded, as happens in point 15 ofFig. 7 with PLR = 4.0%, then the effects of cross trafficon PLR increase exponentially.� The HCF mechanism improves the channel access
function reducing the nominal values of all metricsexcept the time to stream. This means the QoS mecha-nism assures high priority level to video streaming alsoin presence of cross traffic characterized by a high bitrate.� The time to stream does not benefit from the QoS mech-
anism. In fact, it is still distributed in a random way, inagreement with what observed in the normal modescenario. This is reasonable because the packet loss isdue only to buffer overflow, as no air collisions can beexperienced in our test-bed.� It is interesting to observe that high time to stream val-
ues (as measured for experiment number 15) do notinfluence the final video quality score. That is, the bufferlength of VLC is capable of managing these deviationsand providing a good video quality to the final users.
7. Conclusions
The paper has presented an experimental study of theeffects of bandwidth limitations on video streaming overWiFi. A proper measurement test-bed has been designedfor the scope in a controlled environment, i.e. a semi-anec-hoic chamber. The transport layer protocol considered isUDP, which is by far the more used for video streamingover WiFi. Tests have been conducted in two operatingmodes: a normal mode and a QoS mode.
Experimental results have shown a dependence of PLRon the cross traffic rate and packet size, but there is no evi-dence of a dependence of the total streaming time on thecross traffic. This is reasonable considering that UDP is aconnectionless protocol. Regarding VQM, it has been ob-served that high time to stream values does not influencethe final video quality score. That is, the buffer length ofVLC is capable of managing these deviations and providinga good video quality to the final users. Moreover, it hascome out that only when a heavy PLR is experienced the vi-deo quality worsens significantly. On the contrary, if lowPLR values are measured, the final quality falls into anacceptable value range.
Ongoing research activity is focused on measuring wire-less video streaming performance under different QoSassurance strategies, as well as considering more complexscenarios where cross traffic and in-channel interferenceare jointly affecting the communication.
1668 L. Angrisani et al. / Measurement 44 (2011) 1661–1668
References
[1] A.P. Jardosh, K.N. Ramachandran, K.C. Almeroth, E.M. Belding-Royer,Understanding congestion in IEEE 802.11b wireless networks, in:Proc. of Internet Measurement Conference IMC 2005, pp. 279–292.
[2] M. van der Schaar, Y. Andreopoulos, Z. Hu, Optimized scalable videostreaming over IEEE 802.11 a/e HCCA wireless networks under delayconstraints, IEEE Trans. Mobile Comput. 5 (6) (2006) 755–768.
[3] D. Majumdar, G. Sachs, I.V. Kozintsev, K. Ramchandran, Multicastand unicast real-time video streaming over wireless LANs, IEEETrans. Cir. Sys. Video Technol. 12 (6) (2002) 524–534.
[4] J. Kuri, S.K. Kasera, Reliable multicast in multi-access wireless LANs,Wireless Networks 7 (4) (2001) 359–369.
[5] I. Haratcherev, J.T.K. Langendoen, R. Lagendijk, H. Sips, Optimizedvideo streaming over 80211 by cross-layer signaling, IEEE Commun.Mag. (2006). January.
[6] K. Balachandran, S.R. Kadaba, S. Nanda, Channel quality estimationand rate adaptation for cellular mobile radio, IEEE JSAC 17 (7) (1999)1244–1256.
[7] L. Angrisani, S. D’Antonio, M. Esposito, M. Vadursi, Techniques foravailable bandwidth measurement in IP networks: a performancecomparison, Comput. Networks 50 (3) (2006) 332–349 (22 February).
[8] G. Aceto, A. Botta, A. Pescape, M. D’Arienzo, UANM: a platform forexperimenting with available bandwidth estimation tools, in: Proc.of IEEE Symp. on Computers and Communications (ISCC), 2010, pp.174–179.
[9] T.K. Chiew, P. Ferre, D. Agrafiotis, A. Molina, A.R. Nix, D.R. Bull, Cross-layer WLAN measurement and link analysis for low latency errorresilient wireless video transmission, in: Proc. of Intern. Conf. onConsumer Electronics, ICCE 2005, 8–12 January, 2005, pp.177–178.
[10] A. Valera, P.W.Q. Lee, H.-P. Tan, W.K.G. Seah, Zhi Ang Eu, An in-situ measurement approach for IEEE 802.11 wireless multihopnetworks, in: Proc. of Instrum. and Meas. Tech. Conf., 2009 I2MTC2009, Singapore, 5–7 May, 2009.
[11] L. Angrisani, A. Pescapè, G. Ventre, M. Vadursi, Performancemeasurement of IEEE 802.11b-based networks affected bynarrowband interference through cross-layer measurements, IETCommun. 2 (1) (2008) 82–91.
[12] L. Angrisani, M. Vadursi, Cross-layer measurements for acomprehensive characterization of wireless networks in thepresence of interference, IEEE Trans. Instrum. Measur. 56 (4)(2007) 1148–1156.
[13] K.G. Kyriakopoulos, W. Whittow, D.J. Parish, A framework for cross-layer measurements in wireless networks, in: Proc. of the Fifth IEEEAdvanced Inter-national Conference on Telecommunications (AICT2009) part of WebTel 2009, Venice, Italy, 24–28 May, 2009.
[14] R. Li, D.J. Parish, K.G. Kyriakopoulos, A framework for cross-layermeasurement of 3G and Wi-Fi combined networks, in: Proc. of
PGNet 2008: The 9th Annual Post Graduate Symposium on TheConvergence of Telecommunications, Networking & Broadcasting,Liverpool, UK, 23–24 June, 2008.
[15] A. Sheth, C. Doerr, D. Grunwald, R. Han, D. Sicker, MOJO: ADistributed Physical Layer Anomaly Detection System for 802.11WLANs, in: Proc. of MobiSys’06, June 19–22, 2006, pp. 191–204.
[16] J. Yeo, M. Youssef, T. Henderson, A. Agrawala, An accurate techniquefor measuring the wireless side of wireless networks, in: Proc of theIntern. Workshop on Wireless Traffic Measurements and ModellingWiTMeMo 2005, pp. 13–18.
[17] L. Angrisani, A. Napolitano, A. Sona, Are IEEE 802.11 wirelessnetworks appropriate for video streaming in the presence ofinterference? An experimental answer, in: Proc. of I2 MTC 2008,IEEE Int. Instr. and Meas. Tech. Conf., Victoria, Canada, May 12–15,2008, pp. 1722–1727.
[18] L. Angrisani, A. Napolitano, A. Sona, Cross-layer measurements on anIEEE 802.11g wireless network supporting MPEG-2 video streamingapplications in the presence of interference, EURASIP J. WirelessCommun. Network. 20 (10) (2010).
[19] L. Angrisani, K. Kyriakopoulos, A. Napolitano, D.J. Parish, M. Vadursi,W. Whittow, An experimental analysis of the effects of noise on Wi-Fi video streaming, in: Proc. of the IEEE Intern. Instr. and Meas. Tech.Conf. 2010, I2 MTC 2010, Austin (USA), 3–6 May, 2010.
[20] IEEE Standard for Information technology – telecommunications andinformation exchange between systems – local and metropolitanarea networks – specific requirements Part 11: Wireless LANMedium Access Control (MAC) and Physical Layer (PHY)specifications: Amendment 8: Medium Access Control (MAC)Quality.
[21] Y. Ennaji, M. Boulfmalf, C. Alaoui, Experimental analysis of videoperformance over wireless local area networks, in: Proc. of Intern.Conf. on Multimedia Computing and Systems ICMCS ‘09, Morocco,2–4 April, 2009.
[22] http://www.wireshark.org.[24] http://www.videolan.org/vlc/.[25] ITU-T, Recommendation H.264 – advanced video coding for generic
audiovisual services. <http://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-H.264-201003-I!!PDF-E&type=items>.
[26] ITU-R BT.1683, Objective perceptual video quality measurementtechniques for standard definition digital broadcast television in thepresence of a full reference, January 2004.
[27] http://www.grid.unina.it/software/ITG/.[28] http://www.gnu.org/licenses/gpl.html.[29] ITU-R BT.500-11. 2002. Methodology for the subjective assessment
of the quality of television pictures.[30] http://www.rfc-editor.org/in-notes/rfc2544.txt (accessed 23.03.11).[31] T. Alexander, Optimizing and Testing WLANs: Proven Techniques for
Maximum Performance, Newnes, 2007, pp. 75–105 (Chapter 4).
