end to end QoS for video delivery over wireless internet

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End-to-End QoS for Video Delivery Over

Wireless 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 support, including scalable video representation, network-aware end

system, 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-layernetwork-centric so-lution, and then describe the recent advances in network modeling,QoS mapping, and QoS adaptation. The key targets in end-systemcentric approach arenetwork adaptation andmedia adaptation. Inthis paper, we present a general framework of the end-system cen-tric solution and investigate the recent developments. Specifically,fornetwork 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 advanced

research directions.

KeywordsCross-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 expected

to be widely deployed in the near future. As different types

of wireless networks are converging into all IP networks,

i.e., the Internet, it is important to study video delivery over

the wireless Internet. The current trends in the development

of real-time Internet applications and the rapid growth ofmobile systems indicate that the future Internet architecture

will 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: [email protected]; [email protected]; [email protected]).

Digital Object Identifier 10.1109/JPROC.2004.839603

quality of service (QoS)1 requirements [1]. QoS support is a

multidisciplinary topic involving several areas, ranging from

applications, terminals, networking architectures to network

management, business models, and finally the main target,

end users.

Enabling QoS in Internet is difficult, and becomes more

challenging when introducing QoS in an environment in-

volving mobile hosts under different wireless access tech-

nologies, since available resources (e.g., bandwidth, battery

life, etc.) in wireless networks are scarce and dynamically

change over time. For wireless networks, since the capacity

of a wireless channel varies randomly with time, providing

deterministic QoS (i.e., zero QoS violation probability) will

likely result in extremely conservative guarantees and waste

of resources, which is hardly useful. Thus, in this paper, we

only consider statistical QoS [3]. To support end-to-end QoS

for 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 and

link layers support, efficient packet delivery, conges-

tion control, error control, and power control, all affect

the overall QoS.

2) Different applications have very diverse QoS require-

ments in terms of data rates, delay bounds, and packet

loss probabilities. For example, unlike nonreal-time

data packets, video services are very sensitive to packet

delivery delay but can tolerate some frame losses andtransmission errors.

3) Different types of networks inherently have different

characteristics. This is also referred to as network het-

erogeneity. It is well known that Internet is based on

Internet 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

PROCEEDINGS OF THE IEEE, VOL. 93, NO. 1, JANUARY 2005 123


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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 delay

jitter, vary from time to time. An important character-

istic of wireless networks in the future is that there

are 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 is

much 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 the

varying wireless channel errors. This will further in-

crease the dramatic variation of bandwidth and delay

in wireless networks. To make things even more com-

plicated, the end-to-end packet loss in wireless Internet

can be caused by either congestion loss occurred due

to buffer overflow or the erroneous loss occurred in the

wireless link due to channel error.

4) There is dramatic heterogeneity among end users. End

users 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 in

network bandwidth but also meets the heterogeneous

requirements of the end users.

To address the above challenges, one should support

the QoS requirement in all components of the video

delivery system from end to end, which include QoS

provisioning 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 Engineering

Task Force (IETF) community to seek for QoS sup-

port through network layer mechanisms. The most

well-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)/Universal

Mobile Telecommunications System (UMTS) and

IEEE 802.11 have total different mechanisms for QoS

support.

Multilayered scalable video coding from applications.

In scalable coding, the signal is separated into mul-

tiple layers of different visual importance. The base

layer can be independently decoded and it provides

basic video quality. The enhancement layers can only

be decoded together with the base layer and they fur-

ther refine the video quality. Enhancements on lay-

ered scalable coding have proposed to provide further

fine granularity scalability [7], [8], [95]. Scalable videorepresentation provides fast adaptation to bandwidth

variations 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 in

end systems. When network condition changes, the

end systems can employ adaptive control mechanisms

to minimize the impact on user perceived quality.

Power control, congestion control, and error control

are 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 and

spreading gain for a group of users so as to reduce in-

terference [9]. Congestion control and error control are

conducted from the individual users point of view to

effectively combat the congestions and errors occurred

during transmission by adjusting the transmission rate

and allocating bits between source and channel coding

[10], [11].

There have been two approaches in providing the

end-to-end QoS support: the first one is network-centric

QoS provisioning, in which routers/switches, or/and base

stations/access points in the networks provide prioritized

QoS support to satisfy data rate, delay bound, and packet

loss requirements by different applications. In the prioritized

transmission, QoS is expressed in terms of probability of

buffer overflow and/or the probability of delay violation

at the link layer. However, at the video application layer,

QoS is measured by the mean squared error (MSE) and/or

peak-signal-to-noise ratio (PSNR). Thus, one of the key

issues for end-to-end QoS provisioning using network-cen-

tric solution is the effective QoS mapping across different

layer. More specifically, one needs to consider how to model

the varying network and coordinate effective adaptation of

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


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

network-centric solution.

QoS parameters at video application layer and prioritized

transmission system at link layer. In Section II, we will

describe a general framework of a cross-layer architecture

of a network-centric end-to-end QoS support solution and

then review recent developments in individual components

including network QoS support, channel modeling, QoS

adaptation, and QoS mapping.

The second type of approach to provide end-to-end QoS

support is solely end-system centric. In particular, the end

systems employ various control techniques, which include

congestion control, error control, and power control, to max-imize the application-layer video quality without any QoS

support from the underlying network. The advantage of end

system control is that there are minimum changes required in

the core network. The main challenge, however, is how to de-

sign efficient power/congestion/error control mechanisms. In

Section III, we will present a framework that targets at mini-

mizing the end-to-end distortion or power consumption, and

then 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 layer

and link/network layer) have different metrics to measure

quality of service, which brings challenge for end-to-end

QoS provisioning. Fig. 2 shows the general block diagram

of end-to-end QoS support for video delivery in the net-

work-centric cross-layer solution. This solution considers

an end-to-end delivery system for a video source from the

sender to the receiver, which includes source video en-

coding, cross-layer QoS mapping and adaptation, prioritized

transmission control, adaptive network modeling, and video

decoder/output modules. To support end-to-end QoS with

network-centric approach, a dynamic QoS management

system is needed in order for video applications to interact

with underlying prioritized transmission system to handle

service degradation and resource constraint in time-varying

wireless Internet. Specifically, to offer a good compromise

between video quality and available transmission resource,

the key is how to provide an effective cross-layer QoS

mapping and an efficient adaptation mechanism.

A. Network QoS Provisioning for Wireless Internet

QoS provisioning for the Internet has been a very active

area of research for many years. Two different approacheshave been introduced in IETF, which are IntServ [4] and

DiffServ [5], respectively. IntServ was introduced in IP net-

works in order to provide guaranteed and controlled services

in addition to the existing best-effort service. IntServ and

reservation protocols, such as ReSerVation Protocol (RSVP),

have failed to become a practical end-to-end QoS solution

for lack of scalability and difficulty in that all elements in

the network have to be RSVP enable. DiffServ was proposed

to provide a scalable and manageable network with service

differentiation capability. In contrast to the per-flow-based

QoS guarantee in the Intserv, Diffserv networks provide QoS

assurance on a per-aggregate basis.

The Internet research community has been proposing and

investigating different approaches to achieve differentiated

services. In particular, significant efforts have been devoted

to achieve service differentiation in terms of queuing delay

and 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


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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 Partnership

Project (3GPP)2 is the main standard body that defines

and standardizes a common QoS framework for data ser-

vices, particularly IP-based services. 3GPP has defined a

comprehensive framework for end-to-end QoS covering

all subsystems, from radio access network (RAN) through

core network to gateway node (to the external packet data

network) within a UMTS network [6]. 3GPP has also de-

fined four different UMTS QoS classes according to delay

sensitivity: conversational, streaming, interactive, and back-

ground classes.

In wireless local area networks, the original IEEE 802.11

communication modes, namely, Distributed Coordination

Function (DCF) and Point Coordination Function (PCF), do

not differentiate traffic types. IEEE is proposing enhance-

ments in 802.11e to both coordination modes to facilitateQoS support [21]. In Enhanced Distribution Coordination

Function (EDCF), the concept of traffic categories is intro-

duced. EDCF establishes a probabilistic priority mechanism

to allocate bandwidth based on traffic categories. Aiming

to extend the polling mechanism of PCF, Hybrid Coordina-

tion Function (HCF) is proposed. A hybrid controller polls

stations during a contention-free period. The polling grants

each station a specific start time and a maximum transmit

duration. The 802.11e standard will be ratified at the end of

this year. In the mean time, a group of vendors have proposed

Wireless Multimedia Enhancements (WME) to provide an

interim QoS solution for 802.11 networks [21]. WME uses

four priority levels in negotiating communication between

wireless access points and client devices.

B. Cross-Layer QoS Support for Video Delivery Over

Wireless Internet

An ef ficient QoS mapping scheme that addresses

cross-layer QoS issues for video delivery over wireless

Internet 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 wireless

networks; 2) prioritized transmission control scheme that

can derive and adjust the rate constraint of a prioritized

transmission system; and 3) QoS mapping and adaptation

mechanism that can optimally map video application classes

to statistical QoS guarantees of a prioritized transmission

system so as to provide the best tradeoff between the video

application quality and the transmission capability under

time-varying wireless networks.

1) Wireless Network Modeling: One can model a com-

munication channel at different layers, i.e., physical layer

and link-layer (see Fig. 3). Physical layer channel can be

further classified into radio-layer channel, modem-layer

channel, 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 underlying

physical 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-state

Markov chain [23], whose states are characterized by

different BERs. A codec-layer channel can also be mod-

eled by a finite-state Markov chain, whose states can be

characterized by different data-rates, or a symbol being

error-free/in-error, or a channel being good/bad [24]. Zorzi

et al. [24] demonstrated that Markov model is an approxi-

mation on block transmission over a slowly fading wireless

channel.

In general, based on existing physical-layer channel

models, it is very complex to characterize the relationship

between the control parameters and the calculated QoS

measures. This is because the physical-layer channel models

do not explicitly characterize the wireless channel in terms

of the link-level QoS metrics, such as data rate, delay, and

delay violation probability.

Recognizing that the limitation of physical-layer channel

models in QoS support, i.e., the difficulty in analyzing link-

level performances, attempts have been made to move the

channel model up in the protocol stack, from physical-layer

to 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 critical

tool for designing efficient QoS provisioning mechanisms.

2) Prioritized Transmission Control: To achieve differ-

entiated services, a class-based buffering and scheduling

mechanism is needed in the prioritized transmission control

module. In particular, QoS priority classes are main-

tained with each class of traffic being maintained in separate

buffers. Priority scheduling policy is employed to serve

packets of the classes. Under this class-based buffering andpriority scheduling mechanism, each QoS priority class can

obtain a certain level of statistical QoS guarantees in terms

of probability of packet loss and packet delay. Then, the next

step is to translate the statistical QoS guarantees of multiple

priority classes into rate constraints based on the effective

capacity theory [25]. The calculated rate constraints in

turn specify the maximum data rate that can be transmitted

reliably with statistical QoS guarantee over the time-varying

wireless channel. Consequently, video substreams can be

classified into classes and bandwidth can be allocated ac-

cordingly for each class.

The rate constraint of multiple priority classes under a

time-varying service rate channel can be derived according tothe guaranteed packet loss probabilities and different buffer

sizes of each priority class [26]. The statistical QoS guarantee

of each priority class is provided in terms of packet loss prob-

ability based on the effective service capacity theory. In [26],

Kumwilaisaket al. derived the rate constraint of substreams

under a simplest strict (nonpreemptive) priority scheduling

policy.

3) QoS Mapping and QoS Adaptation: QoS mapping

and QoS adaptation are the key components to achieve

cross-layer QoS support in this video delivery architec-

ture. Unlike the adaptive channel modeling module and

prioritized transmission control module, the QoS mappingand QoS adaptation are application-specific. Since the QoS

measure at the video application layer (e.g., distortion and

uninterrupted video service perceived by end-users) is not

directly related to QoS metrics at the link layer (e.g., packet

loss/delay probability), a mapping and adaptation mech-

anism must be in place to more precisely match the QoS

criterion across different layers. Specifically, at the video

application layer, each video packet is characterized based

on its loss and delay properties, which contributes to the

end-to-end video quality and service. Then, these video

packets are classified and optimally mapped to the classes

of link transmission module under the rate constraint. The

video application layer QoS and link-layer QoS are allowed

to interact with each other and adapt to the wireless channel

condition, whose objective is to find the QoS tradeoff, which

simultaneously provides a desired video service of the end

users with available transmission resources.

There have been many studies on the cross-layer design

for efficient multimedia delivery with QoS assurance over

wired 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 common

approach is to partition multimedia data into smaller units

and then map these units to different classes for prioritized

transmission. The partitioned multimedia units are priori-

tized based on its contribution to the expected quality at the

end user while the prioritized transmission system provides

different QoS guarantees depending on its corresponding ser-

vice priority. Servetto et al. [30] proposed an optimization

framework to segment a variable bit rate source to several

substreams that are transmitted in multiple priority classes.

The objective is to minimize the expected distortion of the

variable bit rate source. Shin et al. [13] proposed to priori-tize each video packet based on its error propagation effect

if it is lost. Video packets were mapped differently to trans-

mission priority classes with the objective of maximizing the

end-to-end video quality under the cost and/or price con-

straint. Tan et al. [28] examined the same problem as that

formulated in [13] with different approaches for video prior-

itization. Other types of multimedia delivery over DiffServ

network, such as prioritized speech and audio packets, were

considered by Martin [31] and Sehgal et al. [27].

Considering the stochastic behavior of wireless networks,

[32], [33] introduced a cross-layer design with adaptive QoS

assurance for multimedia transmission where absolute QoS

was considered. In [32], Xiao et al. studied the rate-delaytradeoff curve offered from the lower-layer protocol to the

applications. Then, the application layer selected the oper-

ating point from this curve as a guaranteed QoS parameter

for transmission. These curves are allowed to be changed

as 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 with

time-varying wireless channel condition, in which the appli-

cation was given the flexibility to adapt to the level of QoS

provided by the network. Targeting at scalable video codec

and considering the interaction between layers to obtain the

operating 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 from

one GOP (group of picture) to priority classes such that

the distortion of this GOP is minimized. Second, find a set

of 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 adaptive

to the variation of network condition in wireless Internet.

This adaptation consists of network adaptation and media

adaptation. The network adaptation refers to how many

network resources (e.g., bandwidth and battery power) a

video application should utilize for its video content, i.e.,

to design an adaptive media transport protocol for video

delivery. The media adaptation controls the bit rate of the

video stream based on the estimated available bandwidth

and adjusts error and power control behaviors according to

the 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



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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 Monitordeals with probing and

estimating the dynamic network conditions. The Congestion

Control module adjusts sending rate based on the feedback

information.For media adaptation, considering that different parts of

compressed scalable video bitstream have different impor-

tance level,Network-aware Unequal Error Protection (UEP)

module protects different layers of scalable video against

congestive packet losses and erroneous losses according to

their importance and network status. Network-aware Trans-

mission Power Adjustment module adjusts the transmission

power of the end-system to affect the wireless channel con-

ditions. R-D Based Bit Allocation module performs media

adaptation 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 effect

on perceived video quality, and these are usually caused by

network congestion. Thus, congestion-control mechanism at

end systems is necessary to reduce packet loss and delay.

Typically, for conferencing and streaming video, congestion

control takes the formof rate control. Rate control attempts to

minimize the possibility of network congestion by matching

the rate of the video stream to the available network band-

width.

To deliver media content, several protocols are involved

and some of them were proprietary solutions. Those proto-

cols include the Real Time Transport Protocol (RTP) and

Real 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 Hypertext

Transport Protocol (HTTP).

Since a dominant portion of todays Internet traffic is

TCP-based, it is very important for multimedia streams to be

TCP-friendly, by which it means a media flow generates

similar throughput as a typical TCP flow along the same

path under the same condition with lower latency. There are

two existing types of TCP-friendly flow-control protocols

for multimedia delivery applications: sender-based rate

adjustment and model-based flow control. Sender-based

rate adjustment [10], [39] performs additive increase andmultiplicative decrease (AIMD) rate control in the sender

as in TCP. The transmission rate is increased in a step-like

fashion in the absence of packet loss and reduced multiplica-

tively when congestion is detected. This approach usually

requires the receiver to send frequent feedback to detect

congestion indications, which may potentially degrade the

overall performance. Model-based flow control [40], [41],

on the other hand, uses a stochastic TCP model [42], which

represents the throughput of a TCP sender as a function of

packet loss ratio and round trip time (RTT). One issue that

should be considered for this type of approach is that the

estimated packet loss ratio is not for the next time interval

so as to affect the accuracy of the throughput calculation.

While TCP-friendliness is a useful fairness criterion in

todays Internet, it is possible that future network architec-

tures (in which TCP is either no longer the predominant

transport protocol or has a very bad performance) will allow

or require different definitions of fairness. For example,

fairness definition for wireless networks is still subject to

research since TCP performance in wireless networks is still

need to be improved.

Designing a transport protocol for video transmission over

wireless Internet, several issues related to network condition

estimation should be considered. The most important one is

the 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-friendly

media transport protocols [43], [44] treat any lost packet as a

signal of network congestion and adjust its transmission rate

accordingly. However, this rate reduction is unnecessary if

the 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 time

may be inaccurate and fluctuate greatly. The third issue is the

available bandwidth estimation. There are many studies on

available bandwidth estimation in Internet, and how to apply

those 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 efficient

media transport protocol is to correctly detect whether thenetwork is in congestion or not. Generally there are two

types of methods to distinguish the network status [45],

which are split connection and end-to-end method. In the

split connection method, it requires an agent at the edge of

wired and wireless network to measure the conditions of

two types of networks separately [46], [47]. Specifically, an

agent is needed at every base station in the entire wireless

communication system, which adds excessive complexity

in the actual deployment. The end-to-end method focuses

on differentiating the congestive loss from the erroneous

packet 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 wireless

Internet. It is known that a specific behavior of a packet

in the network reflects the joint effect of several factors.

Considering that the traffic pattern in the Internet itself is a

complicated research topic, finding a good pattern to predict

the behaviors of packets in wireless Internet still requires

some fundamental research.

Yang et al. proposed a different mechanism in [51] to use

the combined link layer and sequence number information to

differentiate the wireless erroneous loss and congestive loss.

The arrival time of the erroneous packets is used to derive

the distribution of lost packets among the erroneous packets

between two back-to-back correctly received packets.

2) Available Bandwidth Estimation: There are two types

of approaches for available bandwidth estimation in media

transport 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 available

bandwidth using the Receiver Based Packet Pair (RBPP)

method [53]. RBPP requires the use of two consecutively

sent packets to determine a bandwidth share sample. The

most recognized scheme in this category is TCP-Westwood

[54], which maintains two estimators, along with a method

to identify the predominant cause of packet loss. Depending

on the loss cause, the appropriate estimator is adaptively

selected. One estimator, called Bandwidth Estimator (BE),

considers each ACK pair separately to obtain a bandwidth

sample, filters the samples, and returns to the (short term)

bandwidth share that the TCP sender is getting from the

network. The other estimator, called Rate Estimator (RE),

measures the amount of data acknowledged during the latest

interval . RE tends to estimate the (relatively longer term)

rate that the connection has recently experienced. Several

media transport protocols, such as SMCC [55]andVTP[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 effective

than FEC. However, FEC has been commonly suggested for

real-time applications due to their strict delay requirements.

Hybrid ARQ scheme proposed in [57] can achieve bothdelay bound and rate effectiveness by limiting the number

of retransmissions. Other hybrid FEC and delay-constrained

ARQ schemes were discussed in [58][60].

Girod and Frber reviewed on the existing solutions for

combating wireless transmission errors in [61]. While their

focus is on cellular networks, most presented protection

strategies can also be applied to the transmission of video

over 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 a

survey of the different video-optimized error resilience tech-

niques that are necessary to accommodate the compressed

video bitstreams [63]. Various channel/network errors can

result in considerable damage to or loss of compressed video

information during transmission. Effective error conceal-

ment strategies become vital for ensuring a high quality

of the video sequences in the presence of errors/losses. A

review of the existing error concealment mechanisms was

given by Wang and Zhu in [64]. In [65], Majumdar et al.

addressed the problem of resilient real-time video streaming

over IEEE 802.11b WLANs for both unicast and multicast

transmission. 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 combined

with FEC.

Scalable video has received lots of attention in recent

years due to its fast adaptation characteristic. For scal-

able video, one way to efficiently combat channel errors

is to employ unequal error protection (UEP) for infor-

mation of different importance. More specifically, strong

channel-coding protection is applied to the base layer data

stream while weaker channel-coding protection is applied

to the enhancement layer parts. Studying how to add FEC to

scalable video coding has gained great interest recently. Joint

work on scalable video coding with UEP for wired network

[66], [67] and wireless communication [68][70] has been

proposed. In [70], a network adaptive application-level error

control scheme using hybrid UEP and delay constrained

ARQ was proposed for scalable video delivery. Current and

estimated round trip time is used at sender side to determine

the maximum number of retransmission based on delay

constraint. In [71], Van der Schaar and Radha discussed the

combination of MPEG-4 FGS with scalable FEC for unicast

and multicast applications, and a new unequal error protec-

tion strategy referred to as Fine Grained Loss Protection

(FGLP) was introduced.



8/12

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 various

layers of the protocol stack, and hence a joint cross-layer

consideration is desirable in order to provide an optimal

overall performance for the transmission of video. A vertical

system integration, referred to as cross-layer protection,

was introduced in [72] that enabled the joint optimization

of the various protection strategies existing in the protocol

stack. Xu et al. developed a cross-layer protection strategyfor maximizing the received video quality by dynamically

selecting the optimal combination of application-layer FEC

and MAC retransmission based on the channel conditions

[73].

C. Joint Power Control and Error Control

In general, there exists tradeoff between maintaining good

quality of video application and reducing average power

consumption, 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 decrease

transmission power and maintain a desired video quality,

more complex compression algorithms and more powerful

channel coding schemes can be applied to source coding and

channel coding, respectively.

The motivation of jointly considering power control and

error 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 can

be represented as .

Case When video compression is performed with

a given power constraint , the power-con-

strained distortion includes both the distortion

by the source rate control and the distortion

caused 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 links

with a given BER and a limited power

constraint , the end-to-end distortion is com-

posed of the distortion by the source rate

control, the distortion caused by the channel

errors, and the distortion caused by the power

constraint (Fig. 5, ). More generally, it can

be denoted as .

From the individual user point of view, some studies on

allocating available bits for source and channel coders are

aiming at minimizing the total processing power consump-

tion under a given bandwidth constraint. Specifically, a low-power communication system for image transmission was

investigated in [74]. A power-optimized joint source-channel

coding (JSCC)approach for video communication over wire-

less channel was proposed in [75].

From the group user point of view, power control adjusts a

group of users transmission powers to maintain their video

quality 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 least

possible power. It is also referred to as resource management

based on the power control technique discussed in [9], [76],

[77], where it is formulated as a constrained optimization

problem 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] and

Zhang et al. [79] made independently is that when the trans-

mission power of one user is changed to achieve its minimal

power 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 from

the optimal state of their power consumptions. Therefore,

due to the multiple access interference, the global minimiza-

tion of power consumption must be investigated from the

group point of view.

D. Rate-Distortion Based Bit Allocation

For video delivery over wired or wireless network, the

most common metrics used to evaluate video quality are the

expected end-to-end distortion and expected end-to-end

power consumption . Here, consists of source distor-

tion and channel distortion . The source distortion is

caused by source coding such as quantization and rate con-

trol. The channel distortion occurs when the packet loss due

to network congestion or wireless link error happened during

the transmission. consists of processing power on the

source 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 the

channel coding, under a given fixed bandwidth capacity so

as to achieve the minimal expected end-to-end distortion or

minimal expected end-to-end power consumption [67], [78].

More specifically, the resource allocation problem can be for-

mulated as follows:



9/12

where is the total bandwidth assigned to source coding

and channel protection, while is the total bandwidth

budget.

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 one

type of packet loss is considered. As discussed earlier, in

wireless Internet the packet losses consist of both conges-

tive losses and erroneous losses, which in turn have different

loss patterns in wireless and wired network parts. Consid-

ering that different loss patterns lead to different perceived

QoS at application level [80], Yang et al. presented a loss dif-

ferentiated based bit allocation scheme [51], in which

the channel distortion is caused by two parts: one is caused

during the transmission over wired-line part of the connec-

tion, , andthe other iscausedduringthe transmission

over the wireless channel, .

IV. CONCLUSION

In this paper, we reviewed recent advances in providing

end-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 the

general cross-layer QoS support architecture for video de-

livery over wireless Internet. This architecture enables one

to perform QoS mapping between statistical QoS guarantees

at the network level to a corresponding priority class with dif-

ferent video quality requirements. In the end-system centric

approach, we described the framework that includes network

adaptation and media adaptation and reviewed several key

components 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 Internet

video systems is a relatively new and active field of research,

in which many issues need further examination. Optimally

allocating resources in this heterogeneous setting presents

many challenges and opportunities. To solve the cross-layer

optimization problems for video transmission, several

components such as: 1) adaptive modulation and channel

coding; 2) adaptive retransmission; and 3) adaptive source

rate control need to be jointly optimized to achieve better

performance. Moreover, this paper is primarily focused on

QoS support in a unicast scenario. Efficient end-to-end QoS

support for multicast video transmission systems [81][84]

is an area that still requires considerable work.

Since it has been recognized that the Internet interdomain

routing algorithm, Border Gateway Protocol (BGP), is not

always 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-layer

multicast [85][88], Web content distribution networks, and

resilient overlay networks (RONs) [89]. Recently, there has

been investigation on providing QoS support mechanism in

overlay networks similar to the one in the Internet. OverQoS

[90] aimed to provide architecture to offer QoS using

overlay network. Service Overlay Networks [91] purchases

bandwidth with certain QoS guarantees from individual net-

work domains via bilateral service level agreement (SLA)

to build a logical end-to-end service delivery infrastructure

on top of existing data transport networks. Unlike the work

on network-based QoS, research for QoS provisioning inapplication layer overlay has been pursued in an ad hoc

manner. Thus, there is considerable room for improvement,

especially in considering the video delivery requirement.

Enabling video transport over ad hoc networks is another

challenging task. The wireless links in an ad hoc network are

highly error prone and can go down frequently because of

node mobility, interference, channel fading, and the lack of

infrastructure. In [92], Wang et al. proposed to combine mul-

tistream coding with multipath transport, to show that path

diversity provides an effective way to combat transmission

error in ad hoc networks. QoS routing [93] and QoS aware

MAC [94] are two types of approaches to provide QoS for adhoc networks from networking point of view. Extending the

cross-layer framework to exploit the video delivery over ad

hoc networks is also a quite interesting research direction.

ACKNOWLEDGMENT

The authors would like to thank Dr. B. Li from the Hong

Kong University of Science and Technology for proofreading

this manuscript.

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Qian Zhang (Senior Member, IEEE) receivedthe B.S., M.S., and Ph.D. degrees from Wuhan

University, 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/Internetmultimedianetworking, wireless communications and net-

working, and overlay networking. She is the inventor of about 20 pendingpatents. Her current research interest includes seamless roaming across

different 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 Memberand Chair of QoSIG of theMultimedia CommunicationTechnicalCommittee 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 in

IEEE Wireless Communication Magazine. Dr. Zhang has recently receivedtheTR 100(MIT Technology Review) Worlds Top Young Innovator Award.



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Wenwu Zhu (Senior Member, IEEE) receivedthe B.E. and M.E. degrees from the NationalUni-

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 19961999.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 TRANSACTIONSON 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 SELECTEDAREAS 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 onWireless Video in IEEE Wireless Communication Magazine. Currently heis Associate Editor for IEEE TRANSACTIONSON 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 Video

Technology 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 Kappa

Nu, Multimedia System and Application Technical Committee and LifeScience Committee in IEEE Circuits and Systems Society, and Multimedia

Communication Technical Committee in IEEE Communications Society.

Ya-Qin Zhang (Fellow, IEEE) received the B.S.and M.S. degrees in electrical engineering from

the 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 Microsofts 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 to

2004. 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 offivehigh-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 T RANSACTIONS ON

CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY from July 1997 to July

1999. 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.


end to end QoS for video delivery over wireless internet

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