Forward error correction (FEC) codes based

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Signal Processing: Image Communication 16 (2001) 745}762
Forward error correction (FEC) codes based multiple
description coding for internet video streaming and multicast
Rohit Puri *, K. Ramchandran , K.W. Lee, V. Bharghavan
EECS Dept, UC Berkeley, USA
ECE Dept, Univ. IL, Urbana, USA
Abstract
In this work, we describe the application of generalized multiple description (MD) source coding architected through
the usage of forward error correction (FEC) codes to achieve robust and e$cient Internet video streaming and multicast
consistent with the existing Internet architecture. We highlight the synergistic interaction between the robust source
coding and packetization strategy and an e$cient as well as responsive, TCP-friendly congestion control protocol linear
increase multiplicative decrease with history (LIMD/H) to achieve robust streaming over the Internet. The proposed
source packetization scheme transforms a loss sensitive layered video bitstream into a robust packet stream so as to
provide graceful resilience to network packet drops. The proposed congestion control mechanism provides low variation
in transmission rate in steady state and at the same time is reactive and provably TCP friendly. Furthermore, exploiting
the fundamental property of a multicast tree, namely, the observation that the multicast tree is a degraded channel, we
propose an elegant application level architecture for e$cient video multicast. We exploit the amenability of the MD
coding strategy to fast transcoding. Our extensive simulation studies for the streaming problem and preliminary results
for the multicast case, bear out the e$cacy of the proposed setup. 2001 Elsevier Science B.V. All rights reserved.
Keywords: Robust multimedia streaming; Application-transport layer interaction; Multimedia multicast
1. Introduction
In the current deployment of the Internet, network routers are typically oblivious to the structure
or content of the packets and treat all packets
equally. While this simple network design has enabled the Internet to scale to its present size, it
adversely impacts multimedia #ows. Typical multi* Corresponding author.
E-mail addresses: [email protected] (R. Puri), [email protected] (K. Ramchandran), [email protected]
crhc.uiuc.edu (K.W. Lee), [email protected]
(V. Bharghavan).
media streams are highly structured, i.e., characterized by a hierarchy of importance layers (e.g.,
I, P versus B frames in MPEG) and hence are
fundamentally mismatched to the existing switching strategies. Furthermore, emerging multimedia
compression standards for image/video coding are
evolving towards a multiresolution (MR) or layered
representation of the coded bitstreams. For
example, there is a strong push in the next-generation image and video compression standards,
JPEG-2000 and MPEG-4, respectively, to support
scalability.
Multiple description (MD) source coding has
recently emerged as an attractive framework for
0923-5965/01/$ - see front matter 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 3 - 5 9 6 5 ( 0 1 ) 0 0 0 0 5 - 4
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R. Puri et al. / Signal Processing: Image Communication 16 (2001) 745}762
robust transmission over unreliable channels. The
essential idea is to generate multiple independent
descriptions (N in number) of the source such that
each description independently describes the
source with a certain desired "delity; when more
than one description is available, they can be synergistically combined to enhance the quality. Note
that while the MR bitstream is sensitive to the
position of losses in the bitstream (e.g., a loss early
on in the bitstream can render the rest of the bitstream useless to the decoder) the MD stream is
insensitive to them and thus has the desired feature
that the delivered quality is dependent only on the
fraction of packets delivered reliably. While most of
the initial work in this area has focussed on the
special case of N"2 descriptions [6,30], there
has been recent interest in the N'2 case also
[8,16,30,20]. For example, [20] presents an e$cient
algorithm that transforms an MR-based prioritized
bitstream into a non-prioritized MD packet stream
for which the delivered quality is a function of only
the number of packets delivered, and is thus well
suited to Internet multimedia transmission. The
proposed strategy uses forward error correction
(FEC) channel codes and incorporates the priority
encoding transmission (PET) [1] philosophy in formulating a systematic, fast and end-to-end ratedistortion optimized algorithm. Details of this are
presented in Section 2.
The `smart end-hosts/simple networka philosophy which has enabled the Internet to scale to its
present size, entrusts to the end user, the responsibility of maintaining transmission rates that are fair
to other connections. Two extreme transport services that have emerged under this model are the
reliable, network-fair transmission control protocol (TCP) [27] and the unreliable `best e!orta
service called the user datagram protocol (UDP)
[18]. TCP is not well suited for multimedia transmission for multiple reasons. First, the feature
of reliability that it o!ers is unnecessary for
multimedia applications and furthermore obtained
at the expense of large round trip delays since
reliability is attained through repeated retransmissions (ARQ: automatic repeat request).
Typical multimedia applications, being loss-tolerant and delay sensitive, are therefore not well
matched to TCP.
Secondly, TCP employs a conventional endto-end congestion control paradigm based on the
linear increase multiplicative decrease (LIMD)
philosophy. The source continuously probes the
network by incrementing its transmission rate (linear increase) when it encounters no loss. However,
upon loss, it throttles down its transmission rate by
a large scaling factor (multiplicative decrease). The
LIMD approach reacts aggressively to packet loss
which enables quick response to the onset of congestion; however it leads to undesirably large variations in the delivered transmission rate even in
steady state. Such a congestion control philosophy
can have an adverse impact on multimedia #ows
since for such applications, `smoothnessa in delivered quality is an important consideration from
the end user's point of view.
The design of multimedia protocols thus dictates
that transmission rate variations be small, while
also requiring the congestion control policy to be
network-friendly, namely, that it should allow
other TCP #ows to co-exist and not grab their fair
share of the bandwidth. In today's Internet, where
most of the tra$c is TCP based, what is thus
required is a congestion control paradigm that
builds on top of UDP and possesses the property of
`TCP-friendlinessa. Equation-based rate control
has been recently proposed [5] as an attractive
option for multimedia Internet transmission. It
has the desirable feature of delivering maximally
smooth, TCP-fair transmission rate. However, it is
characterized by a relatively slow responsiveness to
network dynamics. In this work we present a new
congestion control algorithm, based on linear increase and graded multiplicative decrease, that can
be conceptualized as generalizing both the LIMD
control and equation based rate control approaches. We dub our approach `LIMD with
historya or LIMD/H since it uses history of packet
loss to adapt the control parameters.
Recent work [24,25,28] on multimedia streaming has highlighted the bene"ts of joint design of
the source coding schemes and the transport layer
protocols. Our end-to-end system design subscribes
Subjectively, it has been found that large deviation in delivered picture quality leads to a bad user experience.
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Fig. 1. Block diagram of the end-to-end video transmission system.
prioritized MR bitstream into an N-packet unprioritized MD packet stream using e$cient erasure channel codes. Each description in the MD
stream occupies an entire network packet, thus
the terms `descriptiona and `packeta are used
interchangeably.
2.1. Transcoding mechanism
Fig. 2. Progressive bitstream from the source coder partitioned
into N layers or quality levels.
to this inter-layer interaction philosophy. While the
individual components are novel in their own right,
a key aspect of our approach is their e$cient and
synergistic coordination in an integrated architecture. See Fig. 1 for the end-to-end block diagram of
our streaming system.
The remainder of this paper is organized as follows. Section 2 describes the FEC based robust
source coding (MD-FEC) scheme. Section 3 presents the congestion control algorithm and the interaction between the application and the transport
layer. In Section 4, we evaluate the performance of
our system using the ns-2 network simulator [15].
Section 5 views the source coding scheme as
a `transcoding agenta describing its applicability to
a multicast scenario and presents preliminary results for the same. Section 6 concludes the paper
and o!ers directions for future work.
2. Forward error correction codes based
multiple description coding
In this section, we brie#y describe the mechanics
of the packetization strategy that converts the
The quality pro"le re#ects the target quality (or
equivalently distortion d: lower distortion implies
higher quality and vice versa) when any k out of
N descriptions are received. We will use the notation d(k) to describe the quality pro"le where the ith
entry in d(k) represents the target quality when
i descriptions are received.
Given N and d(k) and a progressive bitstream,
the stream is marked at N positions (see Fig. 2)
which correspond to the attainment of the distortion levels d(k) and is thus partitioned into N sections or resolution layers. The goal is to enable
the ith layer to be decodable when i or more
Bitstreams which have the property of successive re"nability, i.e., new blocks of data re"ne the previous blocks of data are
referred to as multiresolution or scalable bitstreams. Bitstreams
with a large re"nement block size are typically referred to as
layered bitstreams while bitstreams for which the re"nement
block size is, theoretically, of the order of a bit are referred to as
`bit-progressive or embeddeda bitstreams as in the case of the
2-D SPIHT image coder [23]. While the analysis presented in
this section applies directly to the latter category of bitstreams,
it can be easily generalized to the case of layered bitstreams
such as those that can be generated by H.263#/MPEG video
encoders.
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R. Puri et al. / Signal Processing: Image Communication 16 (2001) 745}762
Fig. 3. N-description generalized MD codes using forward error correction codes.
descriptions arrive at the receiver (i.e., when the
number of erasures does not exceed N!i). This
can be attained using the Reed Solomon family of
erasure-correction block codes (An (n,k,d) block
code is de"ned by a length n code with k user
symbols and a minimum distance of d, i.e it can
correct (d!1) erasures. Reed Solomon block
codes have the property of maximum distance
(d"n!k#1), i.e. the whole data can be recovered from any k out of n symbols.), which are
characterized by the optimal code parameters (N, i,
N!i#1) and can correct any (N!i) erasures out
of N descriptions. We split the ith layer into i equal
parts, and apply the (N, i, N!i#1) Reed
Solomon code to get the contribution from the ith
layer or section to each of the N descriptions. The
contributions from each of the N levels or sections
are then concatenated to form the N descriptions
(see Fig. 3). This packetization strategy thus provides the property that the more the number of
packets received, the better the received quality.
We have so far shown the mechanics of our
transcoding algorithm that transforms an MR bitstream into an MD packet stream but have not yet
addressed the question of how to optimize the rate
partitions in the framework. The answer to this of
course depends on the properties of the channel at
hand. We address this question in the following
section.
2.2. Optimization problem statement
In this section, we formulate the optimization
problem. We assume that our model is characterized by q(i), i"!1, 2, N!1, denoting the probability that i#1 out of N packets are delivered to
the destination. We coin the term transmission proxle to refer to the above channel state information.
From operational rate-distortion theory, it follows that distortion is a one-to-one function (D(r))
of the rate r. Hence determining the quality pro"le
d(k) of order N corresponds to "nding the rate
partition R"R , 2, R
of the bitstream (see
,\
Fig. 2).
De"ne the expected distortion ED,
,\
ED"q E# q D(R ),
(1)
\
H
H
H
where E equals the source variance, the distortion
encountered when the source is represented by zero
bits. When the codes as outlined in the above section are used, the total rate used R equals
R
R
R " N
R
1
#
R !R
R
!R
N#2# ,\
,\ N.
2
N
(2)
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Problem statement. Given the number of packets N,
each packet of size ¸ (i.e. a total rate budget"
N¸), an embedded bitstream with rate}distortion
curve D(r), and the transmission pro"le q(i), "nd
R that minimizes ED subject to
R )RH,
(3)
R
R )R )2)R
)R
,
(4)
,\
,\
R !R "k (i#1), k *0, i"1, 2, N!1.
G
G\
G
G
(5)
The constraints (4) and (5) are, respectively, referred
to as the `embeddinga and the `channel coding
constraints. The former arises due to the physical
nature of the bitstream, i.e., a greater number of
source layers would use a greater amount of rate
(see Fig. 2). The latter arises from the fact that in
order to apply an (N, i, N!i#1) Reed Solomon
code to the ith layer, the ith layer should contain
a number of source symbols that is an integral
multiple of i. The solution to this problem based on
Lagrangian optimization has been discussed in detail in [20] and is outside the scope of this paper.
To summarize, we thus presented a non-prioritizing encoding strategy that is ideally suited for
multimedia transmission over the Internet. In the
following section, we deal with the second aspect of
the problem, namely, designing of a TCP friendly
congestion control protocol given that the target
application is multimedia transmission.
Fig. 4. (a) LIMD: "0.5, (b) LIMD/H: "small. Dotted
values represent the average connection throughput.
3.1. Motivation for LIMD/H algorithm
for adapting the transmission rate of a connection
to match the available bandwidth whether the
target application is data-oriented [27] or multimedia [2,22]. Brie#y, LIMD periodically adapts
the sending rate of a connection by gently increasing the rate by a linear constant upon observing
no packet losses (in order to probe for additional
bandwidth), and aggressively decreasing the
sending rate by a large multiplicative constant
upon observing packet losses in order to alleviate
congestion (see Fig. 4).
The trade-o!s of LIMD are quite well known.
While the LIMD approach has been shown to be
robust and asymptotically convergent to fairness
[7], it also reacts identically and aggressively to all
packet losses, both congestion induced and noncongestion induced. When there is actual congestion, LIMD throttles the sending rate su$ciently so
that congestion can be cleared out quickly. But
when the network bandwidth is invariant, periodic
packet losses due to channel probing still induce
Congestion control algorithms that are deployed
in the current Internet typically employ the linear
increase multiplicative decrease (LIMD) paradigm
For TCP, the increase factor is 1 packet/RTT and the
decrease factor is set to a half.
3. Congestion control
In the target environment, a congestion control
algorithm should provide a smooth variation of
transmission rate so as to ensure lesser variation in
delivered multimedia quality. In addition it should
possess features such as quick response to network
dynamics, e$ciency, fairness and TCP-friendliness
[5].
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large rate #uctuations, which is undesirable especially given the target application. An ideal end-toend congestion control algorithm would be that
which comprises of a loss di!erentiation mechanism that classi"es packet losses as being congestion/non-congestion induced and reacts instantly
to the former by adapting to the new network
capacity while throttling gracefully to the latter case.
The congestion control algorithm we present in
this paper called LIMD/H (LIMD with history) is
an augmentation of the basic LIMD approach and
tries to achieve the behavior of an ideal congestion
control algorithm. It features a simple loss di!erentiation mechanism to predict the cause of packet
loss and react accordingly. The LIMD/H congestion control algorithm has the following salient
attributes:
1. LIMD/H uses the history of packet losses in
order to distinguish between congestion-induced
and non-congestion-induced packet losses.
2. LIMD/H reacts gently to non-congestion-induced losses in order to keep the sending rate
variation to minimum, but reacts quickly to the
onset of the congestion.
3. In the steady state it delivers the same average
rate as the LIMD control (with "1 and
"0.5) implemented in TCP would, and therefore is TCP-friendly.
3.2. Framework for congestion control
In our framework, the evolution of the transmission rate occurs over discrete time periods called
epochs. At the end of each epoch, the receiver computes the number of received packets during that
epoch and then sends a congestion feedback message to the sender (via acknowledgements) containing this number. The loss fraction 0(f(1 can
thus be calculated at the sender. Upon receiving the
loss feedback, the sender executes the LIMD/H congestion control algorithm to adjust its sending rate.
3.3. LIMD/H congestion control
We "rst present the rate evolution equations for
LIMD congestion control. Let r denote the sending
rate, f denote the loss fraction, denotes the linear
increase constant, and denotes the multiplicative
decrease constant.
Using LIMD:
If f"0, rQr#.
If f'0, rQr(1!).
LIMD employs aggressive rate throttling for any
packet loss, congestion related or probe related.
Ideally, we want to reduce the sending rate aggressively upon congestion-induced loss and adapt
gracefully to probe-induced loss. The goal therefore
is to set the value of the rate-throttling factor
based on the cause of packet loss. An intuitive
heuristic to predict congestion is the following: if
the packet losses observed during an epoch are
caused by congestion but the sender does not
throttle aggressively enough, then the loss will persist; on the other hand, if the packet loss is a probe
loss, then so long as the sender throttles enough to
account for the loss, there will be no loss in the next
epoch.
Taking these factors into account, we propose
the LIMD/H algorithm that keeps track of a very
simple history parameter, h. The h variable is initially set to 1, is doubled if there is a packet loss in
an epoch, and reset to 1 if there is not. Thus, the
h variable is a very simple mechanism to capture
the history of packet loss in the previous epochs.
Based on the value of the h variable, LIMD/H
exercises a graded multiplicative decrease upon
packet loss:
If f"0, rQr# and hQ1.
If f'0, rQr(1!h) and hQ2h.
In case of LIMD/H, we typically set the multiplicative decrease constant to be small values, e.g. 0.1,
in order to achieve small variations of sending
rate when the available bandwidth is invariant.
LIMD/H thus throttles its transmission rate gently
when there was no packet loss in the previous
epoch, and progressively more aggressively when
previous epochs have also experienced packet loss
(see Fig. 4).
Of course, the LIMD/H algorithm presented
above is a very simple augmentation to LIMD.
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A detailed treatment of this algorithm is presented
in [9,12]. Also in [12], it was shown that the increase/decrease parameters of LIMD/H can be
tuned to achieve approximately the same steady
state throughput as the LIMD congestion control
(with "1 and "0.5) of TCP would. Essentially,
to reduce the variation in the transmission rate,
typically small values of are used in LIMD/H.
Hence to ensure same average rate as say TCP,
needs to be made correspondingly smaller. This
approach to congestion control contrasts that
proposed in [5,19] where the TCP throughput
equation has been used to perform rate based congestion control. Equation-based congestion control, as it is known, measures the round-trip time
and observed packet loss probability to compute
the nearly constant TCP-friendly rate for a #ow.
The advantage of such an approach lies in the fact
that it can provide very smooth rate control, avoiding abrupt changes in sending rate, which can be
well matched to multimedia applications such as
video streaming over the network } to this extent,
its properties are similar to LIMD/H. However, it
is relatively slow to respond to network dynamics.
Another drawback of equation-based congestion
control is that its success depends on accurate
measurements of the connection round-trip
time and the packet loss probability; the latter
is very di$cult to calibrate accurately in a responsive manner since it requires large observation
intervals. A quantitative comparison of these
approaches, is however, outside the scope of this
paper [9,17].
3.4. Coordination between application and
transport layer
To summarize, so far, we have advocated
a state-of-the-art algorithm that o!ers a robust
source encoding option and an e$cient algorithm
at the transport layer (LIMD/H) that o!ers smooth
transmission rate variations that are amenable to
multimedia transmission.
A key aspect of the end-to-end system is the
synergistic interaction between the application and
the transport layer which highlights the value sharing information between the application and the
transport layer. This interaction is e!ected by the
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e$cient transfer of the channel state information
from the transport layer to the application layer
(see Fig. 1). The congestion feedback from the
receiver is available at the transport layer and can
be used to infer the channel state and hence the
transmission pro"le which can then be operated on
by MD-FEC algorithm to deduce the optimal
encoding strategy.
While a wealth of literature is available on sophisticated tra$c models for the Internet, capturing
the dynamics of the Internet channel has proved to
be a di$cult task in general. Driven by our goal of
designing a simple but e!ective interface for transfer of channel state information, we resort to the
proven histogram based adaptive estimation principles that blend long-term averaging and shortterm updates, that have been deployed with great
success in various research areas (ranging from
control systems to adaptive "ltering to arithmetic
coding). Our approach for channel estimation is
measurement-based and simply involves collecting
statistics available from reverse channel feedback
and then conveying them to the application layer
(MD-FEC channel pro"le). This is achieved by
maintaining a pro"le of the relative frequency f (t)
G
of occurrence of each rate sample r(i) (the number
of packets successfully received by the receiver). For
the actual implementation, we maintain a histogram of the rate samples r(i). For updating the
histogram upon availability of new feedback
information, on receiving new feedback information, we add the `newa information to the `olda
information in a weighted fashion where the weight
for the old information has the connotation of
a `forgetting factora which enables faster adaptation to network dynamics. The histogram is interpreted as the transmission pro"le by the MD-FEC
transcoder. Based on our experiments, we "xed the
forgetting factor at 0.875 which worked reasonably
well for all situations.
To summarize, our current mechanisms incur
very low complexity and despite their simple nature
perform well in various simulation scenarios that
we have considered. Since our architecture is
modular in design, it can easily accommodate
sophisticated channel modeling machineries
also which could form a part of possible future
work.
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4. Simulation results
In this section, we present simulation-based performance results of the proposed video transmission scheme. The ns-2 network simulator was used
to implement the LIMD/H congestion control algorithm as a part of the transport layer protocol.
For our simulations, we used a parameterized version of the `Footballa video sequence as encoded
by the state-of-the-art embedded video encoder,
namely the 3-D SPIHTencoder [10]. The frame
size is 352;240 pixels for the luminance with
a subsampling factor of 2 in each of the spatial
dimensions for each of the two chrominance components. Each GOP comprises of 16 frames.
The transmission speed chosen was 1 GOP per
epoch, where each epoch has a duration equal
to 650 ms, which corresponds to approximately
26.7 frames/s.
The details of the performance of the individual
components namely the MD-FEC and the
LIMD/H algorithm are presented in [9,20], respectively, wherein the two respective components were
shown to signi"cantly outperform state-of-the-art
reference systems. (For example, the MD-FEC algorithm was shown to outperform reference systems [8,26] by rate savings of the order of 30%
while delivered identical image qualities). In this
paper, we present the performance of the end-toend system as a whole especially highlighting the
e!ectiveness of the simple interaction between
the application and the transport layer. Our reference transmission systems represent a gradual
evolution from the dumb MR scheme with no
robustness at all to the proposed MD-FEC scheme.
They are:
1. Multiresolution (MR) source encoder,
2. Constant FEC or equal error protection (EEP),
3. Fixed unequal error protection (FUEP).
MR source encoder corresponds to no robustness against packet loss. EEP is the case when
di!erent layers of the prioritized bit stream are
assigned the same level of error protection. FUEP
corresponds to the case when di!erent layers of the
prioritized bit stream are assigned unequal error
protection that is xxed, and therefore does not
adapt to the varying network conditions. An
example of an FUEP system is the priority encod-
ing transmission (PET) [1] scheme applied to encoding of MPEG video [13].
The performance of the video transmission
systems was compared in terms of the measured
delivered quality at the receiver (or peak signal-tonoise ratio (PSNR) in dB) versus time (s). We provide performance results of the candidate robustness mechanisms in a variety of network scenarios
ranging from single hop to multihop topologies to
varying degrees of random loss probabilities to
sudden variations in network capacity.
The random loss model captures the commonly
accepted connection model for a #ow that shares
a large network with a large number of other #ows.
The scenario where the network capacity suddenly
changes illustrates the behavior of the system for
networks with heavy dynamics such as router/link
failure, or upon the occurrence of major network
events such as the NASA path"nder webcast.
4.1. Comparison of the source robustness
mechanisms
In this section, we focus on the performance of
various robustness mechanisms with LIMD/H
congestion control algorithm in a simple network
topology shown in Fig. 5. In all the cases, we tested
the performance of each system when there is random loss in the network channel, and when there is
a sudden congestion due to the introduction of
800 Kbps CBR on cbrPsink during 400}500 s
period. The bottleneck link has a bandwidth of
nearly 2.5 Mbps.
The performance of the MR encoder is shown in
Fig. 6. As expected the MR encoder exhibits extreme fragility and delivers wildly #uctuating quality even in the situation of no random loss, the
Fig. 5. A One-hop Network Topology.
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Fig. 6. Performance of MR: (a) in steady state and upon the introduction of a CBR #ow, (b) 0.01% random loss on the bottleneck link.
Fig. 7. Performance of Equal Error Protection (EEP) in a one hop topology network with no random loss depicted in Fig. 5 for varying
degrees of redundancy: (a) 10% redundancy, (b) 20% redundancy.
amplitude of the #uctuations being more than 5 dB
(Fig. 6). In the situation of random loss, the performance is even worse. Fig. 7 depicts the performance
of the EEP scheme for the case of variation in
network capacity. As expected, as the strength of
the channel codes used is increased, smoother albeit
lower quality is delivered to the destination. In this
case, the quality pro"le exhibits a cliw e!ect as can
be seen for the case corresponding to 10% redundancy via channel codes. In Fig. 8 we observe the
performance of the EEP scheme for situations of
random loss. Once again, the cliw ewect that is
inherent in channel codes is depicted.
The FUEP scheme assigns pre-speci"ed protection to pre-speci"ed source resolution layers. For
instance, in [13], the priority encoding transmission scheme [1] was used for MPEG encoding.
Here the I-frames were encoded with priority 60%,
P-frames with priority 85% and B-frames with
priority 95%. (Protecting a resolution layer with
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Fig. 8. Performance of Equal Error Protection (EEP) in a one hop topology network depicted in Fig. 5 for varying degrees of
redundancy and random loss: (a) 10% redundancy, 0.01% random loss, (b) 20% redundancy, 0.01% random loss, (c) 10% redundancy,
0.1% random loss, (d) 20% redundancy, 0.1% random loss.
priority x% means that the receiver can recover it
from x% of the encoded packets. In channel coding
jargon, x is referred to as the code rate.) As in [13],
in FUEP, we partitioned the source into three
layers. Figs. 9 and 10 compare the performance of
the FUEP scheme and the MD-FEC scheme. We
observe that, in general, the MD-FEC scheme delivers a superior quality in comparison with the
two chosen FUEP schemes. Moreover, the quality
delivered by MD-FEC is smoother than that
delivered by its counterparts. The LIMD/H trans-
mission rate evolution for the four scenarios of no
loss/CBR #ow, 0.01% random loss, 0.1% random
loss and 1.0% random loss is plotted in Fig. 11.
We conclude this section with the following remark. A realistic network scenario would comprise
of all the simulation scenarios presented above. The
MD-FEC/LIMD-H approach owing to its robustness and fast adaptivity properties and its reasonable performance in all the simulation scenarios
described above is well suited for such varying
network situations.
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Fig. 9. Performance comparison between MD-FEC scheme and "xed unequal error protection (FUEP) schemes with rates (60%, 80%,
95%) and (40%, 70%, 95%), respectively, for varying degrees of random loss: (a) MD-FEC, no loss, (b) MD-FEC, 0.01% loss, (c) FUEP
(60%, 80%, 95%), no loss, (d) FUEP (60%, 80%, 95%), 0.01% loss, (e) FUEP (40%, 70%, 95%), no loss, (f) FUEP (40%, 70%, 95%),
0.01% loss.
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Fig. 10. Performance comparison between MD-FEC scheme and "xed unequal error protection (FUEP) schemes with rates (60%, 80%,
95%) and (40%, 70%, 95%), respectively, for varying degrees of random loss: (a) MD-FEC, 0.1% loss, (b)MD-FEC, 1% loss, (c) FUEP
(60%, 80%, 95%), 0.1% loss, (d) FUEP (60%, 80%, 95%), 1% loss, (e) FUEP (40%, 70%, 95%), 0.1% loss, (f) FUEP (40%, 70%, 95%),
1% loss.
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Fig. 11. LIMD/H transmission rate evolution for the one hop network topology: (a) no loss, CBR #ow, (b) 0.01% random loss, (c) 0.1%
random loss, (d) 1% random loss.
4.2. Performance in a large network
In this section, we present results for the performance of the robustness mechanisms in a complex
multi-hop network topology. We only present the
results with FUEP and MD-FEC due to space
constraints. Fig. 12 shows the topology of the network. There is a single video stream (vsPvr) and
7 TCP connections (SiPRi) in the network. The
link capacity and the one-way delay is annotated
on the graph. All the links without annotation were
set to 40 Mbps and 20 ms.
Fig. 13 shows the performance results of the
video stream when encoded by the FUEP and
MD-FEC methods. We observe that in general the
MD-FEC scheme delivers a smoother and marginally higher end-to-end quality than the FUEP
scheme. Although there are occasional spikes in the
delivered quality, in general the delivered quality
is less jittery than that delivered by the FUEP
scheme. Moreover, MD-FEC emerges faster from
the interval of congestion than the FUEP scheme
once again highlighting its adaptivity features. To
summarize, MD-FEC scheme responds reasonably
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well to varying network dynamics and is more
adaptive than either of the reference systems.
5. Application of MD-FEC to multicast
We describe the use of the MD-FEC technique
to achieve robust, e$cient and scalable multicasting of real-time multimedia such as video over the
Internet. Multicasting multimedia data over the
Internet is especially challenging due to the hetero-
Fig. 12. A Multi-hop Network Topology with a single video
#ow. Links without annotation have a bandwidth of 40 Mbps
and a delay of 20 ms.
geneous nature of the Internet environment, wherein di!erent receivers can potentially have diverse
characteristics in terms of processing capability,
and the connection quality in terms of bandwidths
and loss rate.
Any source based approach su!ers from the
problem of inter-receiver fairness, namely given
a heterogeneous set of receivers, `optimala source
encoding for a particular subset of them can potentially starve others if the degree of heterogeneity is
high. An alternative is receiver oriented approaches
using layered multicast technology [4,14,29].
Such an approach assumes a layered source that
transmits di!erent layers of multimedia stream to
distinct multicast groups and each receiver subscribes to the multicast groups depending on its
characteristics. [4,29] generalize this idea to include layered forward error correction codes as
well. Such approaches however su!er from a number of drawbacks. In layered multicasting, receivers
perform `join-groupa experiments subscribe to
extra layers in order to probe for more bandwidth.
When they su!er losses and hence quality degradation they leave the highest layer (the least important layer) that they are joined. Such actions, when
done independently, can potentially lead to destructive interference between receivers that share
common links and can result in #uctuations
in received data quality. Moreover, in practice
join}leave operations are associated with signi"cant
Fig. 13. Multi-hop network performance: (a) MD-FEC, (b) FUEP with code rates 60%, 80%, 95% .
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Fig. 14. (a) Network Topology for a network of 16 nodes (link bandwidths are in Mb), (b) Organization of the multicast tree into groups.
The thick lines represent the bottleneck links.
process overhead at the end hosts in addition to the
video stream resynchronization problems at the
receiver.
Recently there has been work on inference of
multicast network topologies based only on endhost measurements [21]. The idea here is to identify
sets of receivers that share common bottlenecks in
the multicast tree and hence have similar and correlated loss patterns and to group them together
(group formation protocol (GFP), see Fig. 14). This
approach ends up arranging the whole multicast
tree as a hierarchy where groups downstream
necessarily have worse reception than groups upstream of them and di!erent groups are separated
by bottleneck links. Assuming such an architecture
we propose a scheme that could employ the protocol described in [11], wherein a multicast session
receiver upstream of a bottleneck link acts as
a transcoding agent for the receiver group immediately below it. If the receiver group downstream
conveys its channel state to the receiver acting as
the transcoding agent, then that receiver can reencode its received bitstream using the MD-FEC
encoder to optimally match the channel state below. A multicast channel thus would be transformed to a succession of point-to-point channels.
More signi"cantly, the Internet is naturally
evolving towards an ISP domain based architecture. In such an architecture the di!erent domain
are connected by bottleneck links (e.g. the trans
Atlantic cable that connects the ISP on the East
Coast in North America with that of Europe). In
such a situation, MD-FEC has a natural position
at domain edges so that it can optimally re-encode
multimedia bitstreams to match them to the domain downstream, i.e. MD-FEC can thus play the
role of an application level proxy [3].
5.1. Results
In this section we present some of the preliminary simulation based results for the proposed
scheme. The Network Simulator (ns-2) [15] was
used as the simulation platform and the video sequence considered was the Football sequence. We
present results for the network topology in
Fig. 14(a). The reference system is a pure source
based approach wherein the source encodes for the
average transmission pro"le (the average transmission pro"le is obtained by taking the mean of the
transmission pro"les of all the receivers) for all
receivers. For the proposed system, the transcoders
(see Fig. 14) for groups 2, 3 and 4 were placed,
respectively, at nodes 1, 2 and 10, respectively, and
the transmission pro"le for each group was passed
to the corresponding transcoding agent. In Fig. 15
we plot the quality delivered to a typical receiver in
each of the smaller groups for both cases. In this
particular simulation scenario we observe a signi"cant quality di!erence (about 7 dB) delivered to
receiver 1 and about 4 dB to receiver 12 thus illustrating the gains due to transcoding. In our simulations we also observed that for greater heterogeneity
the di!erence was even more noticeable.
We thus presented a novel paradigm for multimedia multicast. The proposed scheme ideally
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Fig. 15. Quality delivered in dB at various receivers in Fig. 14: (a) Receiver 1, (b) Receiver 4, (c) Receiver 7, (d) Receiver 12.
bounds the performance of any multicasting
scheme that uses the same source encoder and can
serve as a benchmark for comparing di!erent
multicasting schemes.
6. Conclusions and future work
In recent years, multiresolution coding has become a very popular paradigm for image/video
source coding. However, the current `best e!orta
network model of the Internet is not well suited for
the transmission of MR bitstreams. Furthermore,
the predominantly-used Linear Increase Multipli-
cative Decrease congestion control paradigm is
mismatched to multimedia transmission.
In order to address these issues, we presented
two components: (i) the MD-FEC transcoding
algorithm that converts MR streams optimally to
non-prioritized MD streams that are able to better
tolerate variations in the frequency and relative
position of packet loss; (ii) the LIMD/H congestion
control algorithm that is better matched to multimedia transmission. Also, we presented a simple
and e$cient mechanism to coordinate the congestion control and source coding algorithms closely
in order to best use the varying network resources
introduced "rst in [12,19].
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For the multicast problem we presented a novel
paradigm where proposed scheme ideally bounds
the performance of any multicasting scheme that
uses the same source encoder and can serve as
a benchmark for comparing di!erent multicasting
schemes. Future work includes re"ning the channel
state estimation procedure possibly through usage
of appropriate tra$c models and also the development of practical protocols that approximate the
multicast scheme discussed above. We also propose
to delve into the issues such as the e!ect of application level bu!ering and error concealment mechanisms on the end-to-end delivered quality.
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