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 sup- port, 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
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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
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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
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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.
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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:
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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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fine-granular-scalability (AMC-FGS) for wireless video, IEEETrans. Circuits Syst. Video Technol., vol. 12, no. 6, pp. 360371,Jun. 2002.
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.
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