End-to-End QoS for Video Delivery OverWireless Internet

QIAN ZHANG, SENIOR MEMBER, IEEE, WENWU ZHU, SENIOR MEMBER, IEEE, AND

YA-QIN ZHANG, FELLOW, IEEE

Invited Paper

Providing end-to-end quality of service (QoS) support is essentialfor video delivery over the next-generation wireless Internet. In thispaper, we address several key elements in the end-to-end QoS sup-port, including scalable video representation, network-aware endsystem, and network QoS provisioning. There are generally two ap-proaches in QoS support: the network-centric and the end-systemcentric solutions. The fundamental problem in a network-centricsolution is how to map QoS criterion at different layers respectively,and optimize total quality across these layers. In this paper, we firstpresent the general framework of a cross-layer network-centric so-lution, and then describe the recent advances in network modeling,QoS mapping, and QoS adaptation. The key targets in end-systemcentric approach are network adaptation and media adaptation. Inthis paper, we present a general framework of the end-system cen-tric solution and investigate the recent developments. Specifically,for network adaptation, we review the available bandwidth estima-tion and efficient video transport protocol; for media adaptation,we describe the advances in error control, power control, and cor-responding bit allocation. Finally, we highlight several advancedresearch directions.

Keywords—Cross-layer, end-system centric, end-to-end QoS,network-centric, video delivery, wireless Internet.

I. INTRODUCTION

With the rapid growth of wireless networks and great suc-cess of Internet video, wireless video services are expectedto be widely deployed in the near future. As different typesof wireless networks are converging into all IP networks,i.e., the Internet, it is important to study video delivery overthe wireless Internet. The current trends in the developmentof real-time Internet applications and the rapid growth ofmobile systems indicate that the future Internet architecturewill need to support various applications with different

Manuscript received January 16, 2004; revised July 20, 2004.The authors are with the Beijing Sigma Center, Microsoft Research

Asia, Beijing 100080, China (e-mail: qianz@microsoft.com; wwzhu@microsoft.com; yzhang@microsoft.com).

Digital Object Identifier 10.1109/JPROC.2004.839603

quality of service (QoS)1 requirements [1]. QoS support is amultidisciplinary topic involving several areas, ranging fromapplications, terminals, networking architectures to networkmanagement, business models, and finally the main target,end users.

Enabling QoS in Internet is difficult, and becomes morechallenging when introducing QoS in an environment in-volving mobile hosts under different wireless access tech-nologies, since available resources (e.g., bandwidth, batterylife, etc.) in wireless networks are scarce and dynamicallychange over time. For wireless networks, since the capacityof a wireless channel varies randomly with time, providingdeterministic QoS (i.e., zero QoS violation probability) willlikely result in extremely conservative guarantees and wasteof resources, which is hardly useful. Thus, in this paper, weonly consider statistical QoS [3]. To support end-to-end QoSfor video delivery over wireless Internet, there are severalfundamental challenges.

1) QoS support encompasses a wide range of technolog-ical aspects. To be specific, many technologies, in-cluding video coding, high-performance physical andlink layers support, efficient packet delivery, conges-tion control, error control, and power control, all affectthe overall QoS.

2) Different applications have very diverse QoS require-ments in terms of data rates, delay bounds, and packetloss probabilities. For example, unlike nonreal-timedata packets, video services are very sensitive to packetdelivery delay but can tolerate some frame losses andtransmission errors.

3) Different types of networks inherently have differentcharacteristics. This is also referred to as network het-erogeneity. It is well known that Internet is based onInternet Protocol (IP), which basically only offers the

1Note that the definition of QoS in itself may be somewhat confusing andhas different implications. We adopt the definition “the ability to ensure thequality of the end user experience” [2] in this paper.

0018-9219/$20.00 © 2005 IEEE

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Fig. 1. Fundamental components for end-to-end QoS support.

best-effort services. Specifically, network conditions,such as bandwidth, packet loss ratio, delay, and delayjitter, vary from time to time. An important character-istic of wireless networks in the future is that thereare mixtures of heterogeneous wireless access tech-nologies co-existed such as wireless local area network(WLAN) access, 2.5G/3G cellular access, and Blue-tooth. Bit-error rate (BER) in a wireless network ismuch higher than that in a wireline network. Moreover,link layer error control scheme, such as automatic re-peat request (ARQ), is widely used to overcome thevarying wireless channel errors. This will further in-crease the dramatic variation of bandwidth and delayin wireless networks. To make things even more com-plicated, the end-to-end packet loss in wireless Internetcan be caused by either congestion loss occurred dueto buffer overflow or the erroneous loss occurred in thewireless link due to channel error.

4) There is dramatic heterogeneity among end users. Endusers have different requirements in terms of latency,video visual quality, processing capabilities, power,and bandwidth. It is thus a challenge to design a de-livery mechanism that not only achieves efficiency innetwork bandwidth but also meets the heterogeneousrequirements of the end users.

To address the above challenges, one should supportthe QoS requirement in all components of the videodelivery system from end to end, which include QoSprovisioning from networks, scalable video presenta-tion from applications, and network adaptive conges-tion/error/power control in end systems. Fig. 1 illus-trates key components for end-to-end QoS support.

• QoS provisioning from networks. The best-effort na-ture of Internet has promoted the Internet EngineeringTask Force (IETF) community to seek for QoS sup-port through network layer mechanisms. The mostwell-known mechanisms are the Integrated Services(IntServ) [4] and the Differentiated Services (DiffServ)[5]. The approaches to providing QoS in wireless net-works are quite different from their Internet counter-parts. General Packet Radio Service (GPRS)/UniversalMobile Telecommunications System (UMTS) andIEEE 802.11 have total different mechanisms for QoSsupport.

• Multilayered scalable video coding from applications.In scalable coding, the signal is separated into mul-tiple layers of different visual importance. The baselayer can be independently decoded and it providesbasic video quality. The enhancement layers can onlybe decoded together with the base layer and they fur-ther refine the video quality. Enhancements on lay-ered scalable coding have proposed to provide furtherfine granularity scalability [7], [8], [95]. Scalable videorepresentation provides fast adaptation to bandwidthvariations as well as inherent error resilience and com-plexity scalability properties that are essential for effi-cient transmission over error prone wireless networks.

• Network adaptive congestion/error/power control inend systems. When network condition changes, theend systems can employ adaptive control mechanismsto minimize the impact on user perceived quality.Power control, congestion control, and error controlare three main mechanisms to support quality of ser-vices for robust video delivery over wireless Internet.Power control is performed collectively from the grouppoint of view by controlling transmission power andspreading gain for a group of users so as to reduce in-terference [9]. Congestion control and error control areconducted from the individual user’s point of view toeffectively combat the congestions and errors occurredduring transmission by adjusting the transmission rateand allocating bits between source and channel coding[10], [11].

There have been two approaches in providing theend-to-end QoS support: the first one is network-centricQoS provisioning, in which routers/switches, or/and basestations/access points in the networks provide prioritizedQoS support to satisfy data rate, delay bound, and packetloss requirements by different applications. In the prioritizedtransmission, QoS is expressed in terms of probability ofbuffer overflow and/or the probability of delay violationat the link layer. However, at the video application layer,QoS is measured by the mean squared error (MSE) and/orpeak-signal-to-noise ratio (PSNR). Thus, one of the keyissues for end-to-end QoS provisioning using network-cen-tric solution is the effective QoS mapping across differentlayer. More specifically, one needs to consider how to modelthe varying network and coordinate effective adaptation of

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Fig. 2. General framework of end-to-end QoS support for video over wireless Internet withnetwork-centric solution.

QoS parameters at video application layer and prioritizedtransmission system at link layer. In Section II, we willdescribe a general framework of a cross-layer architectureof a network-centric end-to-end QoS support solution andthen review recent developments in individual componentsincluding network QoS support, channel modeling, QoSadaptation, and QoS mapping.

The second type of approach to provide end-to-end QoSsupport is solely end-system centric. In particular, the endsystems employ various control techniques, which includecongestion control, error control, and power control, to max-imize the application-layer video quality without any QoSsupport from the underlying network. The advantage of endsystem control is that there are minimum changes required inthe core network. The main challenge, however, is how to de-sign efficient power/congestion/error control mechanisms. InSection III, we will present a framework that targets at mini-mizing the end-to-end distortion or power consumption, andthen review the recent studies on various mechanisms.

II. NETWORK-CENTRIC CROSS-LAYER END-TO-END

QoS SUPPORT

As stated above, different layers (e.g., application layerand link/network layer) have different metrics to measurequality of service, which brings challenge for end-to-endQoS provisioning. Fig. 2 shows the general block diagramof end-to-end QoS support for video delivery in the net-work-centric cross-layer solution. This solution considersan end-to-end delivery system for a video source from thesender to the receiver, which includes source video en-coding, cross-layer QoS mapping and adaptation, prioritizedtransmission control, adaptive network modeling, and videodecoder/output modules. To support end-to-end QoS withnetwork-centric approach, a dynamic QoS management

system is needed in order for video applications to interactwith underlying prioritized transmission system to handleservice degradation and resource constraint in time-varyingwireless Internet. Specifically, to offer a good compromisebetween video quality and available transmission resource,the key is how to provide an effective cross-layer QoSmapping and an efficient adaptation mechanism.

A. Network QoS Provisioning for Wireless Internet

QoS provisioning for the Internet has been a very activearea of research for many years. Two different approacheshave been introduced in IETF, which are IntServ [4] andDiffServ [5], respectively. IntServ was introduced in IP net-works in order to provide guaranteed and controlled servicesin addition to the existing best-effort service. IntServ andreservation protocols, such as ReSerVation Protocol (RSVP),have failed to become a practical end-to-end QoS solutionfor lack of scalability and difficulty in that all elements inthe network have to be RSVP enable. DiffServ was proposedto provide a scalable and manageable network with servicedifferentiation capability. In contrast to the per-flow-basedQoS guarantee in the Intserv, Diffserv networks provide QoSassurance on a per-aggregate basis.

The Internet research community has been proposing andinvestigating different approaches to achieve differentiatedservices. In particular, significant efforts have been devotedto achieve service differentiation in terms of queuing delayand packet loss [12], [13], both of which are of primary con-cern for multimedia applications. Many QoS control mecha-nisms, especially in the areas of packet scheduling [14], [15]and queue management algorithms [16], [17], have been pro-posed in recent years. Elegant theories, such as network cal-culus [18] and effective bandwidths [19], have also been de-veloped. Firoiu et al. provided a comprehensive survey on a

ZHANG et al.: END-TO-END QoS FOR VIDEO DELIVERY OVER WIRELESS INTERNET 125

Fig. 3. Different channel models.

number of recent advances in Internet QoS provisioning in[20].

There have also been many studies related to QoS provi-sion in wireless networks. The Third Generation PartnershipProject (3GPP)2 is the main standard body that definesand standardizes a common QoS framework for data ser-vices, particularly IP-based services. 3GPP has defined acomprehensive framework for end-to-end QoS coveringall subsystems, from radio access network (RAN) throughcore network to gateway node (to the external packet datanetwork) within a UMTS network [6]. 3GPP has also de-fined four different UMTS QoS classes according to delaysensitivity: conversational, streaming, interactive, and back-ground classes.

In wireless local area networks, the original IEEE 802.11communication modes, namely, Distributed CoordinationFunction (DCF) and Point Coordination Function (PCF), donot differentiate traffic types. IEEE is proposing enhance-ments in 802.11e to both coordination modes to facilitateQoS support [21]. In Enhanced Distribution CoordinationFunction (EDCF), the concept of traffic categories is intro-duced. EDCF establishes a probabilistic priority mechanismto allocate bandwidth based on traffic categories. Aimingto extend the polling mechanism of PCF, Hybrid Coordina-tion Function (HCF) is proposed. A hybrid controller pollsstations during a contention-free period. The polling grantseach station a specific start time and a maximum transmitduration. The 802.11e standard will be ratified at the end ofthis year. In the mean time, a group of vendors have proposedWireless Multimedia Enhancements (WME) to provide aninterim QoS solution for 802.11 networks [21]. WME usesfour priority levels in negotiating communication betweenwireless access points and client devices.

B. Cross-Layer QoS Support for Video Delivery OverWireless Internet

An efficient QoS mapping scheme that addressescross-layer QoS issues for video delivery over wirelessInternet includes the following important building blocks:1) wireless network modeling that can effectively model

2www.3GPP.org

the time-varying and nonstationary behavior of the wirelessnetworks; 2) prioritized transmission control scheme thatcan derive and adjust the rate constraint of a prioritizedtransmission system; and 3) QoS mapping and adaptationmechanism that can optimally map video application classesto statistical QoS guarantees of a prioritized transmissionsystem so as to provide the best tradeoff between the videoapplication quality and the transmission capability undertime-varying wireless networks.

1) Wireless Network Modeling: One can model a com-munication channel at different layers, i.e., physical layerand link-layer (see Fig. 3). Physical layer channel can befurther classified into radio-layer channel, modem-layerchannel, and codec-layer channel.

Among them, radio-layer channel models can be classi-fied into large-scale path loss and small-scale fading [22].Large-scale path loss models characterize the underlyingphysical mechanisms (i.e., reflection, diffraction, scattering)for specific paths. Small-scale fading models describe thecharacteristics of generic radio paths in a statistical fashion.Modem-layer channel can be modeled by a finite-stateMarkov chain [23], whose states are characterized bydifferent BERs. A codec-layer channel can also be mod-eled by a finite-state Markov chain, whose states can becharacterized by different data-rates, or a symbol beingerror-free/in-error, or a channel being good/bad [24]. Zorziet al. [24] demonstrated that Markov model is an approxi-mation on block transmission over a slowly fading wirelesschannel.

In general, based on existing physical-layer channelmodels, it is very complex to characterize the relationshipbetween the control parameters and the calculated QoSmeasures. This is because the physical-layer channel modelsdo not explicitly characterize the wireless channel in termsof the link-level QoS metrics, such as data rate, delay, anddelay violation probability.

Recognizing that the limitation of physical-layer channelmodels in QoS support, i.e., the difficulty in analyzing link-level performances, attempts have been made to move thechannel model up in the protocol stack, from physical-layerto link-layer [25], [26]. In [25], an effective capacity (EC)channel model was proposed. The model captures the effect

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of channel fading for the link queueing behavior using a com-putationally simple yet accurate model, thus can be a criticaltool for designing efficient QoS provisioning mechanisms.

2) Prioritized Transmission Control: To achieve differ-entiated services, a class-based buffering and schedulingmechanism is needed in the prioritized transmission controlmodule. In particular, QoS priority classes are main-tained with each class of traffic being maintained in separatebuffers. Priority scheduling policy is employed to servepackets of the classes. Under this class-based buffering andpriority scheduling mechanism, each QoS priority class canobtain a certain level of statistical QoS guarantees in termsof probability of packet loss and packet delay. Then, the nextstep is to translate the statistical QoS guarantees of multiplepriority classes into rate constraints based on the effectivecapacity theory [25]. The calculated rate constraints inturn specify the maximum data rate that can be transmittedreliably with statistical QoS guarantee over the time-varyingwireless channel. Consequently, video substreams can beclassified into classes and bandwidth can be allocated ac-cordingly for each class.

The rate constraint of multiple priority classes under atime-varying service rate channel can be derived according tothe guaranteed packet loss probabilities and different buffersizes of each priority class [26]. The statistical QoS guaranteeof each priority class is provided in terms of packet loss prob-ability based on the effective service capacity theory. In [26],Kumwilaisak et al. derived the rate constraint of substreamsunder a simplest strict (nonpreemptive) priority schedulingpolicy.

3) QoS Mapping and QoS Adaptation: QoS mappingand QoS adaptation are the key components to achievecross-layer QoS support in this video delivery architec-ture. Unlike the adaptive channel modeling module andprioritized transmission control module, the QoS mappingand QoS adaptation are application-specific. Since the QoSmeasure at the video application layer (e.g., distortion anduninterrupted video service perceived by end-users) is notdirectly related to QoS metrics at the link layer (e.g., packetloss/delay probability), a mapping and adaptation mech-anism must be in place to more precisely match the QoScriterion across different layers. Specifically, at the videoapplication layer, each video packet is characterized basedon its loss and delay properties, which contributes to theend-to-end video quality and service. Then, these videopackets are classified and optimally mapped to the classesof link transmission module under the rate constraint. Thevideo application layer QoS and link-layer QoS are allowedto interact with each other and adapt to the wireless channelcondition, whose objective is to find the QoS tradeoff, whichsimultaneously provides a desired video service of the endusers with available transmission resources.

There have been many studies on the cross-layer designfor efficient multimedia delivery with QoS assurance overwired and wireless networks in recent years [13], [26]–[29].The focus has been on the utilization of the differentiated ser-vice architecture to convey multimedia data. The commonapproach is to partition multimedia data into smaller units

and then map these units to different classes for prioritizedtransmission. The partitioned multimedia units are priori-tized based on its contribution to the expected quality at theend user while the prioritized transmission system providesdifferent QoS guarantees depending on its corresponding ser-vice priority. Servetto et al. [30] proposed an optimizationframework to segment a variable bit rate source to severalsubstreams that are transmitted in multiple priority classes.The objective is to minimize the expected distortion of thevariable bit rate source. Shin et al. [13] proposed to priori-tize each video packet based on its error propagation effectif it is lost. Video packets were mapped differently to trans-mission priority classes with the objective of maximizing theend-to-end video quality under the cost and/or price con-straint. Tan et al. [28] examined the same problem as thatformulated in [13] with different approaches for video prior-itization. Other types of multimedia delivery over DiffServnetwork, such as prioritized speech and audio packets, wereconsidered by Martin [31] and Sehgal et al. [27].

Considering the stochastic behavior of wireless networks,[32], [33] introduced a cross-layer design with adaptive QoSassurance for multimedia transmission where absolute QoSwas considered. In [32], Xiao et al. studied the rate-delaytradeoff curve offered from the lower-layer protocol to theapplications. Then, the application layer selected the oper-ating point from this curve as a guaranteed QoS parameterfor transmission. These curves are allowed to be changedas the wireless network environment changes. In [33], it in-vestigated the dynamic QoS framework to adaptively ad-just QoS parameters of the wireless network to match withtime-varying wireless channel condition, in which the appli-cation was given the flexibility to adapt to the level of QoSprovided by the network. Targeting at scalable video codecand considering the interaction between layers to obtain theoperating QoS tradeoff points, in [26], the QoS mapping andadaptation for wireless network was addressed in the fol-lowing two steps. First, find the optimal mapping policy fromone GOP (group of picture) to priority classes such thatthe distortion of this GOP is minimized. Second, find a setof QoS parameters for the priority network, such that the ex-pected video distortion is minimized.

III. END-SYSTEM CENTRIC QoS SUPPORT

To provide end-to-end QoS with end-system solution,the video applications should be aware of and adaptiveto the variation of network condition in wireless Internet.This adaptation consists of network adaptation and mediaadaptation. The network adaptation refers to how manynetwork resources (e.g., bandwidth and battery power) avideo application should utilize for its video content, i.e.,to design an adaptive media transport protocol for videodelivery. The media adaptation controls the bit rate of thevideo stream based on the estimated available bandwidthand adjusts error and power control behaviors according tothe varying wireless Internet conditions.

The general diagram for end-system centric QoS provi-sioning is illustrated in Fig. 4. To address network adap-tation, an end-to-end video transport protocol is needed to

ZHANG et al.: END-TO-END QoS FOR VIDEO DELIVERY OVER WIRELESS INTERNET 127

Fig. 4. General framework for end-to-end QoS provisioning for video over wireless Internet withend-system-centric solution.

handle congestion control in wireless Internet. More specifi-cally, the Adaptive Network Monitor deals with probing andestimating the dynamic network conditions. The CongestionControl module adjusts sending rate based on the feedbackinformation.

For media adaptation, considering that different parts ofcompressed scalable video bitstream have different impor-tance level, Network-aware Unequal Error Protection (UEP)module protects different layers of scalable video againstcongestive packet losses and erroneous losses according totheir importance and network status. Network-aware Trans-mission Power Adjustment module adjusts the transmissionpower of the end-system to affect the wireless channel con-ditions. R-D Based Bit Allocation module performs mediaadaptation control with two different targets, i.e., distortion-minimization and power consumption-minimization.

A. Network Adaptive Congestion Control

Bursty loss and excessive delay have a devastating effecton perceived video quality, and these are usually caused bynetwork congestion. Thus, congestion-control mechanism atend systems is necessary to reduce packet loss and delay.Typically, for conferencing and streaming video, congestioncontrol takes the form of rate control. Rate control attempts tominimize the possibility of network congestion by matchingthe rate of the video stream to the available network band-width.

To deliver media content, several protocols are involvedand some of them were proprietary solutions. Those proto-cols include the Real Time Transport Protocol (RTP) andReal Time Control Protocol (RTCP) [34], Session Descrip-tion Protocol (SDP) [35], Real Time Streaming Protocol(RTSP) [36], Stream Control Transmission Protocol (SCTP)[37], Session Initiation Protocol (SIP) [38] and HypertextTransport Protocol (HTTP).

Since a dominant portion of today’s Internet traffic isTCP-based, it is very important for multimedia streams to be“TCP-friendly,” by which it means a media flow generatessimilar throughput as a typical TCP flow along the same

path under the same condition with lower latency. There aretwo existing types of TCP-friendly flow-control protocolsfor multimedia delivery applications: sender-based rateadjustment and model-based flow control. Sender-basedrate adjustment [10], [39] performs additive increase andmultiplicative decrease (AIMD) rate control in the senderas in TCP. The transmission rate is increased in a step-likefashion in the absence of packet loss and reduced multiplica-tively when congestion is detected. This approach usuallyrequires the receiver to send frequent feedback to detectcongestion indications, which may potentially degrade theoverall performance. Model-based flow control [40], [41],on the other hand, uses a stochastic TCP model [42], whichrepresents the throughput of a TCP sender as a function ofpacket loss ratio and round trip time (RTT). One issue thatshould be considered for this type of approach is that theestimated packet loss ratio is not for the next time intervalso as to affect the accuracy of the throughput calculation.

While TCP-friendliness is a useful fairness criterion intoday’s Internet, it is possible that future network architec-tures (in which TCP is either no longer the predominanttransport protocol or has a very bad performance) will allowor require different definitions of fairness. For example,fairness definition for wireless networks is still subject toresearch since TCP performance in wireless networks is stillneed to be improved.

Designing a transport protocol for video transmission overwireless Internet, several issues related to network conditionestimation should be considered. The most important one isthe estimation of congestion loss ratio. In wireless Internet,the end-to-end packet loss can be caused by either conges-tion loss due to buffer overflow or the erroneous loss oc-curred in the wireless link. Traditional TCP and TCP-friendlymedia transport protocols [43], [44] treat any lost packet as asignal of network congestion and adjust its transmission rateaccordingly. However, this rate reduction is unnecessary ifthe packet loss is due to the error occurred in wireless link,which in turn causes bad performance for end-to-end de-livery quality. The second issue is the round trip time (RTT)estimation. There is large variation in end-to-end delay in

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wireless Internet [52]. Sending only a single acknowledg-ment to measure the RTT during a predefined period of timemay be inaccurate and fluctuate greatly. The third issue is theavailable bandwidth estimation. There are many studies onavailable bandwidth estimation in Internet, and how to applythose schemes for transport protocol design in wireless net-works are now attracting much attention [44], [54].

1) End-to-End Packet Loss Differentiation and Estima-tion: As stated above, the key issue of designing an efficientmedia transport protocol is to correctly detect whether thenetwork is in congestion or not. Generally there are twotypes of methods to distinguish the network status [45],which are split connection and end-to-end method. In thesplit connection method, it requires an agent at the edge ofwired and wireless network to measure the conditions oftwo types of networks separately [46], [47]. Specifically, anagent is needed at every base station in the entire wirelesscommunication system, which adds excessive complexityin the actual deployment. The end-to-end method focuseson differentiating the congestive loss from the erroneouspacket loss by adopting some heuristic methods such as in-terarrival time or packet pair [48]–[50]. This type of solutionexpects a packet to exhibit a certain behavior under wirelessInternet. It is known that a specific behavior of a packetin the network reflects the joint effect of several factors.Considering that the traffic pattern in the Internet itself is acomplicated research topic, finding a good pattern to predictthe behaviors of packets in wireless Internet still requiressome fundamental research.

Yang et al. proposed a different mechanism in [51] to usethe combined link layer and sequence number information todifferentiate the wireless erroneous loss and congestive loss.The arrival time of the erroneous packets is used to derivethe distribution of lost packets among the erroneous packetsbetween two back-to-back correctly received packets.

2) Available Bandwidth Estimation: There are two typesof approaches for available bandwidth estimation in mediatransport protocols.

The first type of approach calculates the available band-width based on the estimated RTT and packet loss ratio.Padhye et al. [42] proposed a formula to calculate the net-work throughput that has been widely adopted [40], [52].

The second type of approach calculates the availablebandwidth using the Receiver Based Packet Pair (RBPP)method [53]. RBPP requires the use of two consecutivelysent packets to determine a bandwidth share sample. Themost recognized scheme in this category is TCP-Westwood[54], which maintains two estimators, along with a methodto identify the predominant cause of packet loss. Dependingon the loss cause, the appropriate estimator is “adaptively”selected. One estimator, called Bandwidth Estimator (BE),considers each ACK pair separately to obtain a bandwidthsample, filters the samples, and returns to the (short term)bandwidth share that the TCP sender is getting from thenetwork. The other estimator, called Rate Estimator (RE),measures the amount of data acknowledged during the latestinterval . RE tends to estimate the (relatively longer term)

rate that the connection has recently experienced. Severalmedia transport protocols, such as SMCC [55] and VTP [56],proposed recently, following the idea of TCP-Westwood.

B. Adaptive Error Control

There are two basic error correction mechanisms, namely,ARQ and FEC. ARQ has been shown to be more effectivethan FEC. However, FEC has been commonly suggested forreal-time applications due to their strict delay requirements.Hybrid ARQ scheme proposed in [57] can achieve bothdelay bound and rate effectiveness by limiting the numberof retransmissions. Other hybrid FEC and delay-constrainedARQ schemes were discussed in [58]–[60].

Girod and Färber reviewed on the existing solutions forcombating wireless transmission errors in [61]. While theirfocus is on cellular networks, most presented protectionstrategies can also be applied to the transmission of videoover other types of wireless networks. In [62], Shan and Za-khor presented an integrated application-layer packetization,scheduling, and protection strategies for wireless transmis-sion of nonscalable coded video. Cote et al. presented asurvey of the different video-optimized error resilience tech-niques that are necessary to accommodate the compressedvideo bitstreams [63]. Various channel/network errors canresult in considerable damage to or loss of compressed videoinformation during transmission. Effective error conceal-ment strategies become vital for ensuring a high qualityof the video sequences in the presence of errors/losses. Areview of the existing error concealment mechanisms wasgiven by Wang and Zhu in [64]. In [65], Majumdar et al.addressed the problem of resilient real-time video streamingover IEEE 802.11b WLANs for both unicast and multicasttransmission. For the unicast scenario, a hybrid ARQ algo-rithm that efficiently combines FEC and ARQ was proposed.For the multicast case, progressive video coding based onMPEG-4 Fine Granularity Scalability (FGS) was combinedwith FEC.

Scalable video has received lots of attention in recentyears due to its fast adaptation characteristic. For scal-able video, one way to efficiently combat channel errorsis to employ unequal error protection (UEP) for infor-mation of different importance. More specifically, strongchannel-coding protection is applied to the base layer datastream while weaker channel-coding protection is appliedto the enhancement layer parts. Studying how to add FEC toscalable video coding has gained great interest recently. Jointwork on scalable video coding with UEP for wired network[66], [67] and wireless communication [68]–[70] has beenproposed. In [70], a network adaptive application-level errorcontrol scheme using hybrid UEP and delay constrainedARQ was proposed for scalable video delivery. Current andestimated round trip time is used at sender side to determinethe maximum number of retransmission based on delayconstraint. In [71], Van der Schaar and Radha discussed thecombination of MPEG-4 FGS with scalable FEC for unicastand multicast applications, and a new unequal error protec-tion strategy referred to as Fine Grained Loss Protection(FGLP) was introduced.

ZHANG et al.: END-TO-END QoS FOR VIDEO DELIVERY OVER WIRELESS INTERNET 129

Fig. 5. Illustration of rate-distortion with/without consideringpower constraint and transmission error.

It has been shown that under general wireless environ-ments, different protection strategies exist at the variouslayers of the protocol stack, and hence a joint cross-layerconsideration is desirable in order to provide an optimaloverall performance for the transmission of video. A verticalsystem integration, referred to as “cross-layer protection,”was introduced in [72] that enabled the joint optimizationof the various protection strategies existing in the protocolstack. Xu et al. developed a cross-layer protection strategyfor maximizing the received video quality by dynamicallyselecting the optimal combination of application-layer FECand MAC retransmission based on the channel conditions[73].

C. Joint Power Control and Error Control

In general, there exists tradeoff between maintaining goodquality of video application and reducing average powerconsumption, including processing power and transmis-sion power at end-systems. From network point of view,multipath fading and multiple access interference (MAI)in wireless network necessitate the use of high transmis-sion power. From video coding point of view, to decreasetransmission power and maintain a desired video quality,more complex compression algorithms and more powerfulchannel coding schemes can be applied to source coding andchannel coding, respectively.

The motivation of jointly considering power control anderror control for video communication comes from the fol-lowing observations on the relationship among rate, distor-tion, and power consumption.

Case According to the rate-distortion theory (Fig. 5,), the lower the source coding rate , the

larger the distortion . More generally, it canbe represented as .

Case When video compression is performed witha given power constraint , the power-con-strained distortion includes both the distortionby the source rate control and the distortioncaused by the power constraint (Fig. 5, ).More generally, it can be denoted as

.Case Considering a more specific scenario, a video

bitstream is transmitted over wireless linkswith a given BER and a limited power

constraint , the end-to-end distortion is com-posed of the distortion by the source ratecontrol, the distortion caused by the channelerrors, and the distortion caused by the powerconstraint (Fig. 5, ). More generally, it canbe denoted as .

From the individual user point of view, some studies onallocating available bits for source and channel coders areaiming at minimizing the total processing power consump-tion under a given bandwidth constraint. Specifically, a low-power communication system for image transmission wasinvestigated in [74]. A power-optimized joint source-channelcoding (JSCC) approach for video communication over wire-less channel was proposed in [75].

From the group user point of view, power control adjusts agroup of users’ transmission powers to maintain their videoquality requirements. Recently, the focus has been on ad-justing transmission powers to maintain a required signal-to-interference ratio (SIR) for each network link using the leastpossible power. It is also referred to as resource managementbased on the power control technique discussed in [9], [76],[77], where it is formulated as a constrained optimizationproblem to minimize the total transmission power or max-imize the total rate subject to the SIR and bandwidth re-quirements. The key observation Eisenberg et al. [78] andZhang et al. [79] made independently is that when the trans-mission power of one user is changed to achieve its minimalpower consumption, its interference to other users varies ac-cordingly. This interference variation will alter other users’receiving SIRs and may result in that their video quality re-quirements cannot be achieved, and then in turn deviate fromthe optimal state of their power consumptions. Therefore,due to the multiple access interference, the global minimiza-tion of power consumption must be investigated from thegroup point of view.

D. Rate-Distortion Based Bit Allocation

For video delivery over wired or wireless network, themost common metrics used to evaluate video quality are theexpected end-to-end distortion and expected end-to-endpower consumption . Here, consists of source distor-tion and channel distortion . The source distortion iscaused by source coding such as quantization and rate con-trol. The channel distortion occurs when the packet loss dueto network congestion or wireless link error happened duringthe transmission. consists of processing power on thesource coding , processing power on the channel coding

, and the transmission power for data delivery .It is well known that channel bandwidth capacity is highly

limited in wireless Internet. Thus, it is very important to ef-ficiently allocate the bits among the source coding and thechannel coding, under a given fixed bandwidth capacity soas to achieve the minimal expected end-to-end distortion orminimal expected end-to-end power consumption [67], [78].More specifically, the resource allocation problem can be for-mulated as follows:

130 PROCEEDINGS OF THE IEEE, VOL. 93, NO. 1, JANUARY 2005

where is the total bandwidth assigned to source codingand channel protection, while is the total bandwidthbudget.

Or

and

where is the end-to-end distortion budget.In all the schemes mentioned above, the erroneous losses

and the congestive losses are treated the same and only onetype of packet loss is considered. As discussed earlier, inwireless Internet the packet losses consist of both conges-tive losses and erroneous losses, which in turn have differentloss patterns in wireless and wired network parts. Consid-ering that different loss patterns lead to different perceivedQoS at application level [80], Yang et al. presented a loss dif-ferentiated – based bit allocation scheme [51], in whichthe channel distortion is caused by two parts: one is causedduring the transmission over wired-line part of the connec-tion, , and the other is caused during the transmissionover the wireless channel, .

IV. CONCLUSION

In this paper, we reviewed recent advances in providingend-to-end QoS support for video delivery over wireless In-ternet from both network-centric and end-system centric per-spectives. In the network-centric solution, we presented thegeneral cross-layer QoS support architecture for video de-livery over wireless Internet. This architecture enables oneto perform QoS mapping between statistical QoS guaranteesat the network level to a corresponding priority class with dif-ferent video quality requirements. In the end-system centricapproach, we described the framework that includes networkadaptation and media adaptation and reviewed several keycomponents in this framework. More specifically, recent de-velopments in congestion control, error control, power con-trol, and – based bit allocation schemes were addressed.

Cross-layer design of heterogeneous wireless Internetvideo systems is a relatively new and active field of research,in which many issues need further examination. Optimallyallocating resources in this heterogeneous setting presentsmany challenges and opportunities. To solve the cross-layeroptimization problems for video transmission, severalcomponents such as: 1) adaptive modulation and channelcoding; 2) adaptive retransmission; and 3) adaptive sourcerate control need to be jointly optimized to achieve betterperformance. Moreover, this paper is primarily focused onQoS support in a unicast scenario. Efficient end-to-end QoSsupport for multicast video transmission systems [81]–[84]is an area that still requires considerable work.

Since it has been recognized that the Internet interdomainrouting algorithm, Border Gateway Protocol (BGP), is notalways able to provide good quality routes between domains,more recently, there have been proposals to establish appli-cation-level overlay networks for multimedia applications.Examples of overlay networks include application-layermulticast [85]–[88], Web content distribution networks, and

resilient overlay networks (RONs) [89]. Recently, there hasbeen investigation on providing QoS support mechanism inoverlay networks similar to the one in the Internet. OverQoS[90] aimed to provide architecture to offer QoS usingoverlay network. Service Overlay Networks [91] purchasesbandwidth with certain QoS guarantees from individual net-work domains via bilateral service level agreement (SLA)to build a logical end-to-end service delivery infrastructureon top of existing data transport networks. Unlike the workon network-based QoS, research for QoS provisioning inapplication layer overlay has been pursued in an ad hocmanner. Thus, there is considerable room for improvement,especially in considering the video delivery requirement.

Enabling video transport over ad hoc networks is anotherchallenging task. The wireless links in an ad hoc network arehighly error prone and can go down frequently because ofnode mobility, interference, channel fading, and the lack ofinfrastructure. In [92], Wang et al. proposed to combine mul-tistream coding with multipath transport, to show that pathdiversity provides an effective way to combat transmissionerror in ad hoc networks. QoS routing [93] and QoS awareMAC [94] are two types of approaches to provide QoS for adhoc networks from networking point of view. Extending thecross-layer framework to exploit the video delivery over adhoc networks is also a quite interesting research direction.

ACKNOWLEDGMENT

The authors would like to thank Dr. B. Li from the HongKong University of Science and Technology for proofreadingthis manuscript.

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Qian Zhang (Senior Member, IEEE) receivedthe B.S., M.S., and Ph.D. degrees from WuhanUniversity, Wuhan, China, in 1994, 1996, and1999, respectively, all in computer science.

She joined Microsoft Research, Asia, Beijing,China, in July 1999. Now, she is the researchmanager of the Wireless and Networking Group.She has published more than 80 refereed papersin international leading journals and key confer-ences in the areas of wireless/Internet multimedianetworking, wireless communications and net-

working, and overlay networking. She is the inventor of about 20 pendingpatents. Her current research interest includes seamless roaming acrossdifferent wireless networks, multimedia delivery over wireless, Internet,next-generation wireless networks, and P2P network/ad hoc network.

Dr. Zhang is a member of the Visual Signal Processing and Communi-cation Technical Committee and the Multimedia System and ApplicationTechnical Committee of the IEEE Circuits and Systems Society. She is alsoa Member and Chair of QoSIG of the Multimedia Communication TechnicalCommittee of the IEEE Communications Society. Dr. Zhang is now servingas Associate Editor of IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY.She is also serving as Guest Editor for a special issue on wireless video inIEEE Wireless Communication Magazine. Dr. Zhang has recently receivedthe TR 100 (MIT Technology Review) World’s Top Young Innovator Award.

ZHANG et al.: END-TO-END QoS FOR VIDEO DELIVERY OVER WIRELESS INTERNET 133

Wenwu Zhu (Senior Member, IEEE) receivedthe B.E. and M.E. degrees from the National Uni-versity of Science and Technology, Changsha,China, in 1985 and 1988, respectively, the M.S.degree from Illinois Institute of Technology,Chicago, in 1993, and the Ph.D. degree fromPolytechnic University, Brooklyn, NY, in 1996,all in electrical engineering.

From August 1988 to December 1990, he waswith the Graduate School, University of Scienceand Technology of China (USTC), and Chinese

Academy of Sciences (Institute of Electronics), Beijing, China. He joinedMicrosoft Research, Beijing, in 1999 as a Researcher in the Internet MediaGroup, and now is Research Manager of Wireless and Networking Group.Prior to his current post, he was with Bell Labs., Lucent Technologies,Murray Hill, NJ, as a Member of Technical Staff during 1996–1999.He has published over 160 refereed papers in various key journals andconferences in the areas of wireless/Internet multimedia delivery, wirelesscommunications and networking, and has contributed to the IETF ROHCWG draft on robust TCP/IP header compression over wireless links. Heis inventor of more than a dozen pending patents. His current researchinterest is in the area of wireless/Internet multimedia communication andnetworking, and wireless communication and networking.

Dr. Zhu served as Guest Editor for the special issues on “StreamingVideo” and special issue on “Wireless Video” in IEEE TRANSACTIONS

ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. He also served asa Guest Editor for the special issue on “Advanced Mobility Managementand QoS Protocols for Wireless Internet” in IEEE JOURNAL ON SELECTED

AREAS IN COMMUNICATIONS. Currently, he is serving as a Guest Ed-itor for the special issue on “Advanced Video Coding and Delivery” inPROCEEDINGS OF THE IEEE, and a Guest Editor for the special issue on“Wireless Video” in IEEE Wireless Communication Magazine. Currently heis Associate Editor for IEEE TRANSACTIONS ON MOBILE COMPUTING, IEEETRANSACTIONS ON MULTIMEDIA, and IEEE TRANSACTIONS ON CIRCUITS

AND SYSTEMS FOR VIDEO TECHNOLOGY, respectively. He received the BestPaper Award in IEEE Transactions on Circuits and Systems for VideoTechnology in 2001. He is also the Chairman of IEEE Circuits and SystemSociety Beijing Chapter and the Secretary of Visual Signal Processingand Communication Technical Committee. He is a member of Eta KappaNu, Multimedia System and Application Technical Committee and LifeScience Committee in IEEE Circuits and Systems Society, and MultimediaCommunication Technical Committee in IEEE Communications Society.

Ya-Qin Zhang (Fellow, IEEE) received the B.S.and M.S. degrees in electrical engineering fromthe University of Science and Technology ofChina (USTC), Hefei, Anhui, China, in 1983and 1985, respectively, and the Ph.D. degree inelectrical engineering from George WashingtonUniversity, Washington, DC, in 1989.

He is currently the Corporate Vice Present ofthe Mobile and Device Group at Microsoft Cor-poration, Redmond, WA. He is responsible forproduct development of Microsoft’s Mobile and

Embedded Division, including the WinCE operating system, Smartphone,PocketPC, and other Windows Mobile platform and devices. Prior to that,he was the Managing Director of Microsoft Research Asia from 1999 to2004. Previously, he was the Director of the Multimedia Technology Labora-tory, Sarnoff Corporation, Princeton, NJ (formerly David Sarnoff ResearchCenter and RCA Laboratories). Prior to that, he was with GTE LaboratoriesInc., Waltham, MA, from 1989 to 1994. He has been engaged in researchand commercialization of MPEG2/DTV, MPEG4/VLBR, and multimediainformation technologies. He has authored and co-authored over 200 ref-ereed papers in leading international conferences and journals, and has beengranted over 40 U.S. patents in digital video, Internet, multimedia, wireless,and satellite communications. Many of the technologies he and his teamdeveloped have become the basis for start-up ventures, commercial prod-ucts, and international standards. He serves on the Board of Directors of fivehigh-tech IT companies and has been a key contributor to the ISO/MPEGand ITU standardization efforts in digital video and multimedia.

Dr. Zhang served as the Editor-In-Chief for the IEEE TRANSACTIONS ON

CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY from July 1997 to July1999. He was the Chairman of the Visual Signal Processing and Commu-nications Technical Committee of the IEEE Circuits and Systems (CAS)Society. He serves on the editorial boards of seven other professional jour-nals and over a dozen conference committees. He has received numerousawards, including several industry technical achievement awards and IEEEawards, such as the CAS Jubilee Golden Medal. He was named “ResearchEngineer of the Year” in 1998 by the Central Jersey Engineering Councilfor his “leadership and invention in communications technology, which hasenabled dramatic advances in digital video compression and manipulationfor broadcast and interactive television and networking applications.” He re-cently received The Outstanding Young Electrical Engineer of 1998 award.

134 PROCEEDINGS OF THE IEEE, VOL. 93, NO. 1, JANUARY 2005