Journal of Network and Computer Applications 34 (2011) 1518–1529

Contents lists available at ScienceDirect

Journal of Network and Computer Applications

1084-80

doi:10.1

n Corr

E-m

journal homepage: www.elsevier.com/locate/jnca

Multimedia applications over metropolitan area networks (MANs)

Rashid Mehmood a,n, Raad Alturki a, Sherali Zeadally b

a School of Engineering, Swansea University, Swansea SA2 8PP, UKb Department of Computer Science and Information Technology, University of the District of Columbia, 4200, Connecticut Avenue, N.W., WA DC 20008, USA

a r t i c l e i n f o

Article history:

Received 15 March 2010

Received in revised form

3 August 2010

Accepted 4 August 2010Available online 8 August 2010

Keywords:

QoS

Multimedia

Network

Voice

WLAN

Markov modelling

45/$ - see front matter Crown Copyright & 2

016/j.jnca.2010.08.002

esponding author.

ail address: R.Mehmood@swansea.ac.uk (R. M

a b s t r a c t

Multimedia applications have been the key driving force in converging fixed, mobile and IP networks. A

major hurdle in the realisation of this convergence is obtaining Quality of Service from a heterogeneous,

best-effort service network. Interactive voice requires strict bounds on delay, jitter and packet losses,

for Different Network Traffic Intensity, whereas video adds significant bandwidth requirements to

the network, while Internet only makes its best effort to deliver a packet. Hence, the end-to-end QoS

management of heterogeneous networks supporting multimedia services is of paramount importance.

We present an empirical performance study of multimedia applications over 802.11 networks within

metropolitan area networking environments. Specifically, we study the QoS performance of Voice over

IP (VoIP) applications over 802.11-based networks, while sharing the network resources with HTTP and

video applications. Using the OPNET simulator, we simulate several realistic application traffic

scenarios, and we investigate the performance of VoIP applications by analyzing QoS parameters, such

as delay, jitter, MOS, and packet loss ratio. Subsequently, the performance characteristics data of the

network, which we obtain through simulations, are used to build a Markov model of the network

performance to extend our analysis and gain further insight into the network performance dynamics.

Crown Copyright & 2010 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Multimedia applications have been the key driving forcebehind the convergence of fixed, mobile and IP networks. Thecurrent trends suggest that, in the future, multimedia services willbe delivered over multiple heterogeneous network platforms, allemploying IP-based technologies. The Internet is a looselycoupled distributed collection of autonomous networks, whichdelivers traffic of different types on a best-effort basis. In contrast,multimedia applications usually have stringent Quality of Service(QoS) requirements. Various IP-based heterogeneous wired/wire-less networking technologies are being connected to the evergrowing Internet infrastructure. But it is still a significantchallenge for such an infrastructure (supporting various serviceson a best-effort basis) to support QoS requirements of variousapplications, particularly those involving continuous media (suchas voice and video). To manage networks for end-to-end QoSprovisioning, understanding the QoS requirements of multimediaapplications and the performance of heterogeneous networks,carrying such multimedia traffic, is of paramount importance.

In this work, we investigate the performance of multimediaapplications over 802.11 networks within the metropolitan area

010 Published by Elsevier Ltd. All

ehmood).

networking environment. Specifically, we study the performanceof VoIP over 802.11 networks, while concurrently sharingnetwork resources with other applications involving differenttraffic types. We focus on an interactive voice application due toits commercial importance in driving network convergence, andbecause it requires strict bounds on delay, jitter, and packet loss.We employ video streaming applications to add backgroundtraffic to the simulated network, because video has significantbandwidth requirements. HTTP traffic is used to populate thenetwork, since it is a common application that requires somebandwidth, but has soft QoS requirements. Using OPNET, wesimulate various scenarios for VoIP communications betweendifferent nodes connected through 802.11 and metropolitan areaIP networks. We use voice, video and HTTP applications, togetherin different configurations, to achieve realistic scenarios. Usingthe scenarios, we study the end-to-end VoIP performance, usingmetrics such as network delay, jitter, packet loss ratio, andMean Opinion Score (MOS) (ITU-T, 2003). The study is furtherextended by using the performance characteristics data obtainedthrough simulations to build and investigate a Markov modelof the network performance. This paper extends our earlier workon multimedia performance over metropolitan area networks(Alturki et al., 2009).

The rest of the paper is organised as follows. Section 2 givesthe necessary background along with a brief review of theliterature on end-to-end QoS modelling and management and

rights reserved.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–1529 1519

VoIP support over Wireless Local Area Networks (WLANs). Section3 describes the network test bed used and our measurementprocedures. Section 4 presents our performance results. Finally,in Section 5, we make some concluding remarks and outline ourfuture work.

2. Related works and contributions of this work

2.1. QoS modelling and management, and QoS-enabling

architectures

Rapid developments in computing and communication tech-nologies during the past decade or so have led to an unprece-dented growth in multimedia content generation and multimediadelivery over the Internet. Multimedia which includes text,images, voice and video is being used in all possible ways forbusinesses, social interactions and professional communications.This is in turn leading to the development of a whole new range ofapplications and services based on multimedia content. Examplesof emerging multimedia services include mobile TV, onlinegaming, video telephony, mobile video conferencing, video mailsand multimedia messaging service. These next generation multi-media services have paved the way for network convergence,and have placed additional stricter QoS demands on the globalIP-based networking infrastructure.

In this context, QoS for an application relates to a set ofparameters, which can be used to define service requirements forthis particular application. Some common QoS parameters forapplications include delay, jitter (variation in delay), throughput,and (tolerance in) packet losses. Networks add delay, jitter andpacket losses due to, for example, limited bandwidth availability,buffering and switching delays, lack of buffer space andtransmission errors. An application can use its QoS parametersto negotiate QoS with the underlying network. The networkdesigner or an operator can consider these QoS parameters fornetwork design purposes, for negotiating service level agree-ments, or to develop and implement policies and proceduresrequired to guarantee service level agreements. The networkoperators usually have QoS control functions, such as admissioncontrol, resource reservation and policing, to ensure that the QoSrequirements of applications requesting admission to the networkare fulfilled.

Most QoS measures are additive. They accumulate over all theconstituent components of a network in the communication path.The end-to-end QoS means that the QoS requirements of anapplication are guaranteed throughout all the components of anetwork that are being used to support the application. Forexample, a Voice over IP (VoIP) application between two endhosts connected to two separate WLANs via a Wide Area Networkrequires QoS guarantees from both the WLANs, the WAN, and anyother network component in the connection path. IPv4, thecurrent protocol which binds the Internet together, is unable toprovide end-to-end QoS guarantees for multimedia applications.This has led to many developments and standardisation activitiesto provide end-to-end QoS over Internet, including the IPv4–IPv6evolution. QoS architectures that have been developed to supportend-to-end QoS include Integrated Services (Braden et al., 1994),and Differentiated Services, (Black et al., 1998), and Muti-ProtocolLabel Switching (Rosen et al., 2001). The Integrated Services, orIntServ, architecture is based on fine grained, flow-based methodsto provide end-to-end QoS, using the Resource ReservationProtocol (RSVP). Differentiated Services, or DiffServ, on theother hand, provides course grained, class-based approach toguarantee QoS, using differentiated services code point (DSCP)byte. MPLS is being widely deployed in the Internet backbone.

In MPLS, the packet forwarding process is done by means oflabel swapping. Since labels are short and have fixed length,MPLS can achieve high efficiency compared to conventionalIP routing, where the longest prefix matching is used (Rosenet al., 2001).

QoS support over networked systems gained importance in theearly 1990s generating huge amount of research results to date.The QoS research results generated over the last decades could beclassified into architecture-specific (vertical) or domain-specific(horizontal). Architecture-specific work includes, for example,efforts to improve the Media Access Control (MAC), networking,and transport layer technologies. IntServ, DiffServ, and TCP arenotable examples of architecture-specific research. The domain-specific research was carried out to address QoS support fordifferent applications and services; H.323, video and voice codecsare prime examples of this work. Another direction taken bysome QoS research efforts has been the modelling and character-ization of the Internet traffic dynamics and end-to-end delays, asdiscussed in several recent works (Paxson, 1999; Sommers et al.,2008; Bolot, 1993).

In the context of network characterization, there have beenmany studies on modelling the Internet based on randomgraphs and, relatively recently, as scale-free networks. Zhou’s,(2009) recent article on ‘‘why the Internet is so ‘small’?’’ providesa review of three fundamental properties of the Internet: itfollows power-law degree distribution; it exhibits disassortativemixing; and features the rich-club phenomenon (Zhou andMondragon, 2004) (describing the fact that high-degree nodes,‘‘rich’’ nodes, are tightly coupled with other rich nodes forming acore group or club). The correctness and accuracy of Internetmodels is crucial in end-to-end performance studies and design ofnetworks and applications.

2.2. VoIP over wireless local area networks

The convergence of data and voice networks, ubiquity of WiFinetworks in public private and public places, developments inmetro/access technologies for wireless broadband, and variousbusiness models for voice over IP services had spurred the needfor VoIP performance studies, to increase its capacity and toimprove its QoS. According to a 2009 Gartner report (Tole andAkshay, 2009), by 2019, half of the mobile voice traffic will becarried end-to-end using VoIP and, in next five to ten years (from2009), mobile VoIP services will have a strong impact on thecommunications market.

In this paper, we have studied network performance formultimedia support, including video and HTTP traffic. However,our focus in this paper has been to analyze VoIP performance,while the network capacity is being shared by various levelsof multimedia and data traffic. We therefore review, inthis section, the literature relevant to VoIP performance overWireless LANs.

Several studies can be found in the literature that estimate,measure, or improve VoIP performance over 802.11 WLANsand other wireless networks. These studies can be differentiatedon the basis of their evaluation methodologies: i.e., analytical(e.g. Garg and Kappes, 2003b; Hegde et al., 2005; Harsha et al.,2006), simulation based (Veeraraghavan et al., 2001; Gargand Kappes, 2003a, b) or experimental (Anjum et al., 2003; Toeglet al., 2005; Dangerfield et al., 2006; Dangerfield et al., 2007;Verkaik et al., 2009). Some studies have considered multiplemethodologies to verify their analyses (Markopoulou et al., 2003;Garg and Kappes, 2003b; Hole and Tobagi, 2004; Medepalliet al., 2004; Shin and Schulzrinne, 2009). The studies on VoIPperformance can also be classified on the basis of the study goals,

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–15291520

for example: (a) some studies have focused on finding and/orimproving VoIP capacity1 of WLANs (Crow et al., 1997; Kopsel andWolisz, 2001; Garg and Kappes, 2003a, b; Smavatkul et al., 2003;Markopoulou et al., 2003; Hole and Tobagi, 2004; Toegl et al.,2005; Edo et al., 2009); (b) some have compared the effects ofvarious voice codecs on VoIP capacity and QoS (Hole and Tobagi,2004); (c) others have studied the effects of MAC (e.g. DistributedCoordination Function (DCF) algorithm) and physical layers ofWLANs on VoIP performance (Dangerfield et al., 2006; Danger-field et al., 2007; Stoeckigt and Vu, 2009; Shin and Schulzrinne,2009). We present a brief review of these various approaches tostudy and/or improve VoIP performance below.

Markopoulou et al. (2003) presented a study on the assess-ment of the Internet backbones in supporting the quality of voicecommunications based on delay and loss measurements takenover wide area backbone networks. They characterized andassessed VoIP performance of different types of typical Internetpaths, using a model developed by them. They concluded thatalthough some Internet paths sufficiently support VoIP traffic, alarge number of Internet paths exhibit poor VoIP performance,mainly due to high delay and jitter. They further reported that thereasons for some of the Internet paths being unable to providesatisfactory VoIP performance are mostly related to reliability andnetwork operation, and in these cases the performance can bebrought to a satisfactory standard by taking some appropriatemeasures at the end systems.

Hole and Tobagi (2004) presented a simulation study on VoIPcapacity of IEEE 802.11b WLANs for varying channel conditions,delay constraints and 2 voice codecs (G.729 and G.711). TheWLAN was set up to use the Distributed Coordination Function(DCF) of the IEEE 802.11b MAC protocol. An analytical upperbound on the network capacity was derived under the assump-tions that zero collisions, zero errors, and that all frames arrive atthe playout buffer before the playout time limits. The analyticalupper bound was verified by simulations, using the ns2 simulator.The simulations confirmed the validity of the analytical boundunder good channel qualities and weak or absent delay con-straints. The delay constraints were set by the playout buffer atthe receiver, which dropped packets that incurred end-to-enddelays greater than the threshold value (e.g. 150 ms). The capacityof the network was shown to be highly sensitive to thecomponent delay budgets for wireless networks and packetiza-tion delays. The quality of voice was evaluated, using thesubjective Mean Opinion Score (MOS) metric. The G.711 codecwas able to provide an MOS score of up to 4 due to its relativelyhigher intrinsic MOS compared to G.729. The G.729 codechowever, though limited to the MOS score of 3.5, was able toprovide higher capacity over the wireless network.

Many VoIP capacity analysis studies (Crow et al., 1997; Kopseland Wolisz, 2001; Garg and Kappes, 2003a, b; Smavatkul et al.,2003) similar to the work by Hole and Tobagi (2004) are found inthe literature. The main differences among these works were interms of the data rates, the performance measures of interest, andthe performance evaluation methodologies. For instance, VoIPcapacities over an IEEE 802.11b WLAN were studied in Garg andKappes (2003a) by Garg et al., using an experimental setupcontaining the WLAN, laptops and PCs. They reported that a singlecell of IEEE 802.11b network can support up to a maximum of sixVoIP calls, using the ITU G711a-Law codec with 10 ms of voice perReal-Time Transport (RTP) packet. They further reported thateach VoIP connection (ITU G711a-Law codec with 10 ms voice)

1 The theoretical capacity for VoIP traffic can be defined as the maximum

number of calls that are allowed simultaneously for a certain channel bit rate (see

e.g. Shin and Schulzrinne, 2009).

had the effect of reducing the bandwidth by 900 Kbps. Garg et al.extended their work in (Garg and Kappes, 2003b) by developingan analytical model for further VoIP capacity analysis.

Toegl et al. (2005) presented an implementation of VoIP oversatellite links. The QoS performance was monitored and analyzed,using the Ethereal network protocol analyzer. They presentedVoIP QoS results using two network setups, one with satellitelinks and the other by bypassing the satellite links with a wiredlink. The VoIP performance over satellite links was reported tohave a delay (RTT) of 615 ms, jitter of 68 ms, and an almost zeropacket loss rate (2 out of 2570 packets).

Dangerfield et al. (2007) demonstrated using real measure-ments over a WLAN test bed significant improvements in voicecapacity by exploiting the Transmission Opportunity (TXOP)parameter. The TXOP parameter is defined in IEEE 802.11estandard (IEEE Standard, 2005), which is an approved amendmentto the IEEE 802.11 standard. The IEEE 802.11e standard defines aset of QoS enhancements for WLAN applications throughmodifications to the MAC layer by allowing configurations of anumber of MAC parameters.

Stoeckigt and Vu (2009) extended the work of Dangerfieldet al. (2007). They used simulations and an analytical modelto propose and analyze improved VoIP performance over anIEEE 802.11 WLAN. It was shown that an increasing TXOPparameter value for an Access Point (AP) can significantly improvethe voice capacity of an IEEE 802.11 WLAN. The effects of theTXOP parameter and the size of the buffer at the AP on themaximum number of calls the 802.11 WLAN can support werediscussed and a threshold maximum value for the buffer size wasobtained. Based on the analysis presented in the paper, a closedform expression was derived and proposed for the maximumnumber of voice calls as a function of TXOP. Furthermore, asimple formula for the voice capacity estimation in WLANs wasproposed.

Edo et al. (2009) presented results of VoIP experiments over a802.11g WLAN over a University Campus environment, using theOpen Source PBX & Telephony platform, Asterisk, and smart-phones. They analyzed the VoIP performance using variousparameters including delay, jitter, packet losses, roaming andeffective bandwidth, and they concluded that their Universitywireless network setup can theoretically (in ideal situations)support up to 144 phones (connections) per Access Point (AP),using the G.711 codec.

Verkaik et al. (2009), motivated by ill effects of VoIP on TCPtraffic, present the Softspeak software extensions to enable anefficient use and sharing of the network by VoIP traffic. They claimthat their proposed software extensions significantly reduce theimpact of VoIP on TCP traffic; this is achieved without degradationto the VoIP QoS, and as claimed by the authors that their approachactually improves key QoS metrics for VoIP.

Shin and Schulzrinne (2009) reported results on the VoIPcapacity over IEEE 802.11b WLANs using their ORBIT test bed,simulations as well as its theoretical capacities. Using their ORBITtest bed, they identified a number of factors, which they argued,are usually not taken into account in simulation and analyticalstudies. These factors included preamble size, transmission ratecontrol, packet generation offset among VoIP sources, PHY datarate of ACK frames, retry limit and the network buffer size atthe Access Points (APs). The identification and configuration ofthe identified parameters allowed them to have agreementbetween the results obtained through the test bed experiments,simulations and theoretical capacities. They reported, based ontheir studies, that an IEEE 802.11b (11 Mbps) can support up to15 VoIP CBR calls at 64 Kbps each and 38 VoIP calls for VBRtraffic. They also measured the VoIP capacity for the accesscategories introduced in the IEEE 802.11e standard and analyzed

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–1529 1521

the effects of TCP protocol on VoIP calls, using the IEEE 802.11estandard.

2.3. contributions of this work

Our approach in this paper is to simulate and evaluate realisticmultimedia applications scenarios for WLANs over a metropolitanarea network with a fairly good number and type of applications,both elastic and non-elastic. We evaluate the VoIP capacity ofWLANs over MAN for a set of different applications (as maytypically be found in realistic situations), so that the mutualaffects of network applications can be taken into account.Although we populate the networks with VoIP, Video and HTTPapplications, our focus in this paper is to analyze VoIP perfor-mance in particular.

The trends in applications, devices and networking technologiesare towards convergence. It is therefore highly, likely that a WLANwill have to share multiple different multimedia (and possiblyother) applications, including VoIP. Most of the studies that exist inthe literature have focussed on studying capacity and performanceof VoIP applications in isolation to other applications, i.e. thenetworks are populated by the traffic that only belongs to VoIPcalls. These studies are therefore unable to capture the dynamics ofrealistic networking environments, because they do not take intoaccount the mutual effects that various multimedia applicationswill have on each other, while sharing a single network resource.While there are some studies that have reported on VoIPperformance while sharing the network resources with otherapplications, we believe that those studies are limited in theirapproach to setting up the applications and networking scenarios(e.g. the number and types of applications, and the analysesare limited). Also, the number of studies that have specificallylooked at the end-to-end performance of VoIP on WLANs over ametropolitan area network are extremely limited.

Furthermore, our approach here is to also use the performancecharacteristics of the applications/networks that are obtainedthrough simulations data to build a Markov model and extend thenetwork analysis (as presented in Section 4.3). The benefits aremanifold: the analytical approach we have taken is computation-ally inexpensive and is easy to automate compared to oursimulations. Consequently, it allows us to gain additional insightand build a much bigger performance portfolio of the networkdynamics at a much reduced computational cost. Since we projectthe network performance (gained through simulations), thisapproach also has the potential to allow us to counter-verify thesimulation and analytical results. Finally, we argue that ourapproach is computationally green.

Fig. 1. Test bed network structure.

Table 1Traffic types and properties.

Voice (Kbps) Video (Mbps) HTTP (Kbps) Total (Mbps)

Low G723.1 (5.3) 21.6 167 21.8

Medium GSM-FR (13) 28 168 28.3

High G.711 (64) 33.6 169 34.4

3. Test bed: network architecture and applications

This section describes the simulation test bed environmentincluding the network topology and the various experimentalscenarios, which we have used to study the performance of VoIPapplications over WLAN within a metropolitan area network.We first describe the network topology and architecture followedby a description of multimedia traffic, including voice, video anddata that we used in our performance tests. The network andapplications have been simulated using the Optimized NetworkEngineering Tools (OPNET) Modelling software.

3.1. The network architecture

As we mentioned earlier, the main goal of this work is to studythe performance of VoIP applications over wireless local area

networks within metro environments. In particular, we are interestedin exploring VoIP application performance in Metropolitan AreaNetwork (MAN) environments, where the two end hosts accessthe network through 802.11 Wireless LANs. Furthermore, ourinterest is in investigating VoIP over 802.11 networks underrealistic scenarios where the network bandwidth is being sharedby other applications, including video and HTTP traffic. Networksare usually heavily loaded with video, data and other traffic.Hence, in particular, we are interested in investigating VoIPperformance under heavy background traffic load. This study, forinstance, could be useful for an organisation considering bypass-ing toll for the intra-city telephony services while deploying VoIPand other multimedia applications within a city. The structure ofthe network, which we have considered in this paper, is depictedin Fig. 1. The network consists of two WLANs which are connectedto a common MAN via 1 Gbps Ethernet. We have used 802.11g(54 Mbps) for each of the two WLANs. For the MAN, we have usedan abstract simulation model which allows us to set differentparameters for the network, including packet discard ratio andlatency. A total of 10 nodes are associated to each of the WLANs.These 10 nodes are randomly located within an area of350�350 m2 around each of the two WLANs. The 20 nodesinteract with each other across the network, using differentapplications which are discussed next.

3.2. Network applications

The network is populated with three different kinds ofapplications: video, HTTP and voice. We use three different trafficintensity levels for each of the video, HTTP and voice applications;these are listed in Table 1. The traffic intensity levels are dividedinto ‘Low’, ‘High’ and ‘Medium’, given in separate rows in Table 1.Rows 2–4 outline the details for each application specific to thethree traffic intensity levels. For example, Row 4 lists that thevoice codec used for the ‘High’ intensity traffic case is G.711 with6.4 Kbps bit rate; the video and HTTP traffic in the ‘high’ intensitycase are 33.6 Mbps and 167 Kbps, with a net traffic of 34.4 Mbps.Similarly, Rows 2 and 3 give the details relevant to the ‘Low’ and‘Medium’ traffic intensity levels. In each of these cases, 10 nodesfrom one WLAN are connected to the 10 nodes in the other WLAN.Each node in a WLAN is connected to a node in the other WLAN,and hence there are a total of 30 connections, 10 each for HTTP,video and voice. The voice codecs used in this paper are brieflydescribed next. G.723.1 is a compression technique that can beused for speech or other audio at a low bit rate of 5.3 or 6.3 Kbps.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–15291522

GSM-FR (Global System for Mobile communications—Full Rate) isthe codec used for speech coding in GSM systems. Its bit rate is13 Kbps. G.711 uses PCM (Pulse Code Modulation) technique at64 Kbps bit rate to encode voice.

4. Performance results and analysis

We now present the results obtained through simulating thenetwork described in Section 3. It is worth noting that the size ofthe network in terms of the number of network nodes has beenkept fixed for all simulation tests. The input parameters used inthese simulations include three different voice codecs, and threedifferent traffic intensity levels for each of the video and HTTPapplications, as shown in Table 1. In addition, there are 12different variation levels in (latency, packet discard ratio) pair forMAN. We have performed well over 100 network simulations byusing different input configurations obtained through variationsin these input parameters’ values. As noted earlier, although wehave considered HTTP and video traffic, our main performanceanalysis focus is on an interactive voice. We have thereforecollected through simulations and analyzed five QoS parametersfor an interactive voice. These are end-to-end delay, jitter,throughput, packet loss ratio, and Mean Opinion Score (MOS).In the following sections, we present and discuss the results intwo subsections. First, we discuss the network results for voice

HighM

PCMGSM

G723.1

Packet End-to-En

Voice Quality

Fig. 2. Delay vs. codecs and ba

Voice Jitter (m

HighMe

PCMGSM

G723.1ba

Voice Quality

Fig. 3. Jitter vs. codecs and ba

applications while keeping constant the settings for the metronetwork. Next, we analyze an interactive voice performanceagainst variations in the metro network environment. Finally, inSection 4.3, we use queuing theory to present further analysis ofthe network performance.

4.1. Fixed settings for metro network

Figs. 2–6 depict the results for the five QoS parameters forthe voice applications. Note that although the network ispopulated with voice, video and HTTP applications, the resultspresented in Figs. 2–6 are for the voice applications. Fig. 2plots the average end-to-end delay (in seconds) for the voiceapplication as observed by a voice packet during transmissionfrom the source node in one WLAN to the destination node in theother WLAN across the metro network. There are a total of12 values plotted for the end-to-end delay, associated withdifferent values for the input parameters. The lowest the networkdelay, the better is the network performance. The best resultsare on the rightmost side of the graph, where for ‘No’ backgroundtraffic the end-to-end delay for the network is almost zero forall three voice codecs. The value for the delay increases as weincrease the amount of background network traffic (as shownin Fig. 2). The delay has the highest value of 8.88 s for thecase, where the PCM (G.711) codec was used to encode the voice

ediumLow

No

0123456789

background traffic

d Delay (sec)

ckground network traffic.

0.000.050.100.150.200.250.300.350.40

ilisecond)

diumLow

No

ckground traffic

ckground network traffic.

HighMedium

LowNo

PCMGSM

G723.1background traffic

Voice Quality

75%

80%

85%

90%

95%

100%

Throughput

Fig. 4. Throughput vs. codecs and background network traffic.

HighMedium

LowNo

0%

3%

5%

8%

10%

13%

15%

PCMGSM

G723.1background traffic

packet loss ratio

Voice Quality

Fig. 5. Packet loss ratio vs. codecs and background network traffic.

HighMedium

LowNo

0

1

2

3

4

PCMGSM

G723.1background traffic

MOS Value

Voice...

Fig. 6. Mean opinion score vs. different codecs and background network traffic.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–1529 1523

under ‘High’ background traffic (video and HTTP) (as shownin Table 1). The end-to-end delay for the G.723.1 codec under‘High’ background traffic is 6.24 s, which is slightly lower thanthe delay for the PCM codec. We speculate that this is probablybecause of the higher bit rate of PCM (64 Kbps for each of the10 nodes).

Similarly, Figs. 3–6 depict the results for other QoS parameters.Fig. 3 illustrates jitter (variation in end-to-end delay) for thevoice application in milliseconds. As is the case for the end-to-enddelay results, jitter for ‘No’ and ‘low’ background traffics isalmost zero. However, as we increase the amount of backgroundnetwork traffic, the jitter value increases for ‘Medium’ and ‘High’

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–15291524

background traffic, reaching to a maximum of 0.35 ms. Fig. 4illustrates the throughput for the interactive voice application.The throughput is shown as a percentage ratio in all the graphspresented here (percentage ratio of the total traffic received to thetotal traffic sent). As before, the network throughput worsenswith the increase in the network traffic. The throughput for allthree codecs with ‘No’ background traffic is 100%. In case of ‘Low’background traffic, the throughput for all three codecs is above99.9%. The worst case throughput for voice is 86.14% for the PCMcodec under ‘High’ background traffic. Usually, 86% throughputmay be considered a fairly good value for Internet; however, forvoice application, this number is too low compared to theexpectations (usually anything below 99% is not good). Fig. 5gives the packet loss ratio in percentage for the voice application.The loss ratio is zero for ‘Low’ and ‘No’ background traffic, but theloss rises for ‘Medium’ and ‘High’ background traffic levels. Theexpected packet loss ratio for voice applications should be closeto zero and should not exceed 3% (ITU-T, 2001); therefore, weobserve that the interactive voice performance is acceptablefor the ‘No’ and ‘Low’ background traffic levels; it is unacceptableotherwise.

Fig. 6 plots the mean opinion score (MOS) values for the voiceapplication. The MOS is a subjective measurement representingthe quality of digital multimedia, including video, voice, or audio.The multimedia quality as the MOS is measured as a real numberin the range 1–5; the lowest perceived quality is 1, and 5represents the perfect quality. The P.800 (ITU-T, 1996) recom-mendation of ITU-T specifies methods to perform subjectivequality tests to determine MOS. We note in Fig. 5 that the MOSvalues for ‘No’ and ‘Low’ backgrounds are reasonable, butotherwise the MOS values (less than 1) obtained demonstratethat the VoIP quality degrades significantly under medium andhigh network traffic load (according to ITU-T RecommendationP.800, MOS values 1 and 2 are bad and poor, respectively).

Fig. 7 plots an input traffic intensity (bit rate in Mbps) and thethroughput for the entire network (earlier graphs depicted onlyvoice performance). There are a total of 5 plots in the figure: threeof these plots represent the traffic produced by the threeapplications (voice, video and HTTP), the fourth gives the totaltraffic intensity, and the fifth gives the throughput obtainedagainst varying network traffic intensity. The total network trafficintensity and total network throughput are also shown in Fig. 7.We note in the graph that initially the traffic intensity and the

2721.621.121.00.60.10.1intensity M

internsity rate (Mbps)

throughput (Mbps)

voice (Mbps)

video (Mbps)

http (Mbps)

Fig. 7. Traffic bit rate and backgro

throughput lines are on each other showing that all the traffic sentto the network is delivered with 100% throughput. However, asthe traffic reaches approximately 21 Mbps, the two lines startdiverging with the throughput reaching its lowest value of2.4 Mbps against the total input traffic of 33.6 Mbps. This isexpected, because the total theoretical capacity of the WLAN is54 Mbps.

4.2. Varying the metro network environment

This section analyses the performance of voice applicationagainst different variations in the metropolitan network environ-ment, which connects the two WLANS and the nodes. Thesevariations are obtained by varying the input parameter values forthe metro network latency and packet discard ratio. Fig. 8 plotsthe voice end-to-end delay against variations in traffic intensityand packet discard ratio. We note that the delay increasessignificantly with an increase in the traffic intensity; however, itremains relatively almost constant against changes in the packetdiscard ratio. Fig. 9 plots end-to-end delay against traffic intensityand network latency. As before the delay increases with theincrease in traffic intensity, but remains relatively constantalmost against metro network latency. The reason for such resultsis because the change in network latency (in MAN environments)is quite low compared to the voice delay caused by the limitationsof the WLAN throughput capacity. Figs. 10–13 depict results forvoice throughput, end-to-end delay, jitter, and MOS againstvariations in packet discard ratio and latency for the metronetwork.

4.3. Performance analysis using Markov models

This section presents additional analysis of the simulationresults, using Markov models. Markov Chains and queuing theoryare widely used techniques for performance evaluation intelecommunications, and many areas of science and technologyas well as human sciences. See e.g. Wang et al. (2003), Xiang et al.(2007), Siddique and Kamruzzaman (2008), Gao et al. (2008),Komatsu and Shioda (2009) for Markov chains and queuingtheory models of multimedia performance in WLAN basednetworks.

0

5

10

15

20

25

30

35

40

33.633.133.028.027.5.4bps

und network traffic intensity.

0

1

2

3

4

5

6

7

8

9

0.0%0.5%

1.0%2.0%

traffic intensity (Mbps)

dela

y (s

ec)

Fig. 8. Delay vs. bit rate and discard ratio for different network traffic intensity.

21.0

21.627.5

33.033.6

0

1

2

3

4

5

6

7

8

9

24

8traffic intensity (Mbps)

dela

y (s

ec)

Latency

Fig. 9. Delay vs. traffic intensity and latency for different network traffic intensity.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–1529 1525

The analyses presented earlier had only discussed performanceat discrete input traffic intensities with constant number ofconnections between the two endpoint networks. The analysispresented in this section explores the network performance for avarying number of connections between the endpoint networks atdiscrete input traffic intensities. We only analyze the throughputperformance in this section. However, the methodology used hereis easily applicable to other QoS measures such as delay and jitter.

Consider the traffic profiles listed in Table 1: Row 2 gives theprofile for ‘Low’ traffic, Row 3 for ‘Medium’ traffic and Row 4 liststhe details for the ‘High’ traffic profile. We build 3 separateMarkov Chains, one each for each of the three profiles listed inTable 1. We consider a simple scenario where the requests toestablish connections between two nodes on the far side of the

network (i.e., a connection between two nodes each from adifferent WLAN) arrive in application triplets (voice, HTTP, video).That is, each triplet connection request comprises 3 connectionrequests, one connection each for voice, HTTP and video. Thesetriplets have different bit rate values depending on the 3 trafficprofiles listed in Table 1. For example, for the Markov Chain forthe traffic profile listed in Row 3, the triplet consists of (GSM-FR,16.8 Kbps HTTP, 2.8 Mbps video). The arrival rate is variedfrom zero arrivals to a maximum of 10 arrivals per time unit.The departure rate is constant with 10 departures per unit.

There are a total of 11 states in each Markov model. State 1implies no connection in the system, state 2 implies one tripletconnection in the system, and state 11 implies 10 connections inprogress in the system. We build the transition rate matrix Q of

24

8

97.0%

97.5%

98.0%

98.5%

99.0%

99.5%

100.0%

0.5%1.0%

2.0%Latency

Throughput

Discard Ratio

Fig. 10. Throughput vs. discard ratio and network latency.

24

8

0.1

0.102

0.104

0.106

0.108

0.11

0.112

0.114

0.5%1.0%

2.0% Latency

Packet End-to-End Delay (sec)

Discard Ratio

Fig. 11. Delay vs. discard ratio and network latency.

24

8

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.5%1.0%

2.0%Latency (milisecond)

Voice Jitter (milisecond)

Discard Ratio

Fig. 12. Voice jitter vs. discard ratio and network latency.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–15291526

24

8

2

2.1

2.2

2.3

2.4

2.5

0.5%1.0%

2.0% Latency

MOS Value

Discard Ratio

Fig. 13. Mean opinion score vs. discard ratio and latency.

0 1 2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

100.00099.96

99.92

99.88

99.84

99.80

0

1

2

3

4

5

6

7

8

9

10

Stea

dyst

ate

Prob

abili

ty

Throughput and Steady State Probabilities (Traffic Profile 'Low')

Throughput (%)

Arrival Rate

Fig. 14. Throughput and steady state probabilities for the network under ‘low’ traffic profile.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–1529 1527

each Markov model and solve the following system of linearequations for the vector p

pQ ¼ 0:

The vector p is the steady state vector and comprisesprobability distribution of the system to be in each of the states.The system of equations can be solved either by direct or iterativemethods. Markov models arising from most real life systems aretypically large and sparse, and hence iterative methods areusually employed for the system solution. We have used Gauss–Seidel iterative method (Barrett et al., 1994) to solve the system oflinear equations for these Markov models. Further details of thesolution methods for Markov Chains, background on Markovmodelling and further references can be found in the earlier workof the authors (Mehmood, 2004; Mehmood and Crowcroft, 2005).

Figs. 14–16 depict the throughput and steady state probabilitiesresults obtained by defining Markov models for each of the threetraffic profiles. Let us consider Fig. 14 first which plots the results forthe ‘Low’ traffic profile (shown in Table 1). Note in the figure thatthe probabilities for the various system states are given on thevertical axis, and the state probability numbers and throughput areplotted on the two horizontal axes. The states vary from 0 to 11(as explained earlier), and the throughput varies between 100% and99.8%. The figure illustrates the fact that for zero or low arrival ratesthe probability of obtaining near 100% throughput is higher.However, as we move towards higher arrival rates (towards 10triplet connections), the system throughput will decrease to aminimum of 99.83% throughput and the system probability tohave lower throughput will also increase. Figs. 15 and 16 illustratesimilar results for the ‘Medium’ and ‘High’ profiles. The minimum

0 1 2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

100.00099.66

99.32

98.98

98.64

98.30

0

1

2

3

4

5

6

7

8

9

10

Stea

dyst

ate

Prob

abili

ty

Throughput and Steady State Probabilities (Traffic Profile 'Medium')

Throughput (%)

Arrival Rate

Fig. 15. Throughput and steady state probabilities for the network under ‘medium’ traffic profile.

0 1 2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

100.00097.29

94.58

91.86

89.15

86.44

0

1

2

3

4

5

6

7

8

9

10

Stea

dyst

ate

Prob

abili

ty

Throughput and Steady State Probabilities (Traffic Profile 'High')

Throughput (%)

Arrival Rate

Fig. 16. Throughput and steady state probabilities for the network under ‘high’ traffic profile.

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–15291528

throughput for the system in Fig. 15 is 98.31% and for the systemdepicted by Fig. 16 is 86.44%.

We conclude Section 4 with the observation that it is possibleto support interactive voice applications with high quality underfairly light background traffic, over WLANs connected by ametropolitan area network. Such high voice quality is determinedby low packet losses (0% or o1%), high availability, high MOS, andlow delays (for instance ITU-T G. 1010 (ITU-T, 2001) specifies150 ms one-way delay upper bound). However, the resultsdemonstrate that the QoS for voice telephony greatly deteriorates,after a certain point, with an increase in background networktraffic. We note that the QoS starts deteriorating at the pointwhen the network is loaded with traffic bandwidth, exceeding

almost a third of the WLAN capacity. We have used 802.11g at54 Mbps in these experiments and the voice performance hasbeen found acceptable. The prospects for voice telephony seemeven more promising because the near future will see highcapacity 802.11n, efficient radio spectrum usage, better signalprocessing algorithms, and intelligent MAC protocols.

5. Conclusion

Multimedia is the key driver for the convergence of fixed,mobile and IP networks. Internet, the IP network, is a looselycoupled distributed collection of autonomous networks which

R. Mehmood et al. / Journal of Network and Computer Applications 34 (2011) 1518–1529 1529

employs a best-effort service policy. Multimedia applicationsusually have stringent quality of service requirements. A majorhurdle in enabling network convergence is to deliver QoS from abest-effort service (IP) network. Therefore, understanding QoSrequirements of multimedia applications and the performance ofheterogeneous networks carrying multimedia is of great interestto researchers, designers, and implementers. In this paper, wehave explored performance of VoIP traffic over 802.11 WLANswithin a metropolitan area network environment. We focussed onan interactive voice because of its commercial importance indriving network convergence, and also because it requires strictbounds on delay, jitter and packet loss. Voice, video and HTTPapplications, used together in different configurations, providedrealistic scenarios to analyze the end-to-end VoIP performanceusing metrics, such as delay, jitter, packet loss ratio, throughputand MOS in the presence of a various mix of applications, trafficintensity levels, and voice codecs. Furthermore, the networkperformance was analyzed for different latencies and discard ratiosettings for the MAN environment. Subsequently, the knowledgeof the network characteristics that we have obtained throughsimulation based studies was used to build a Markov model of thenetwork performance, allowing us to gain further insight intothe network performance dynamics. We conclude that interactivevoice applications could be supported on WLANs in a metroenvironment, provided the background traffic is kept withinapproximately a third of the WLAN capacity. In the future, weplan to add detailed models for metro and wide area networks inorder to better understand the dynamics of multimedia applica-tions in such networking environments.

Acknowledgements

We thank the anonymous reviewers for their suggestions andfeedback that helped us to improve the quality of this paper.

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