STREAMING LOW-DELAY VIDEO OVER AIMD
TRANSPORT PROTOCOLS
Ahmed Abd El Al, Tarek Saadawi, Myung Lee
Department of Electrical Engineering, City College and Graduate Center of City University of New York
Convent Avenue at 140
th
Streey, New York, NY 10031, USA
Keywords: Media Adaptation, Low-Delay Video, SCTP.
Abstract: In this paper, we present adaptation strategies for low-delay video streams over Additive-Increase
Multiplicative- Decrease (AIMD) transport protocols, where we switch among several versions of the coded
video to match the available network bandwidth accurately, and meet client delay constraints. By
monitoring the application buffer at the server, we estimate the current and future server buffer drain delay,
and derive the transmission rate to minimize client buffer starvation. We also show that the adaptation
accuracy can be significantly improved by a simple scaling to transport protocol send-buffer size. The
proposed mechanisms were implemented over Stream Control Transmission Protocol (SCTP) and evaluated
through simulation and real Internet traces. Performance results show that the adaptation mechanism is
responsive to bandwidth fluctuations, while ensuring that the client buffer does not underflow, and that the
quality adaptation is smooth so that the impact on the perceptual quality at the client is minimal.
1 INTRODUCTION
While the majority of traffic on the Internet today is
comprised of TCP flows, conventional wisdom
holds that TCP is unsuitable for “low-delay” traffic
due to its lack of throughput guarantees and
insistence on reliability (Krasic, 2001). For these
reasons, there have been many proposals for new
transport protocols for the purpose of solving the
video transport problem over the Internet. These
protocols need to be TCP-friendly to ensure that
they will not cause network collapse. However,
proving a new transport protocol to be TCP-friendly
can be difficult, because the dynamics of TCP
congestion control is extremely complex (Hsiao,
2000).
AIMD-based transport protocols experience rate
variations for two distinct reasons -- the first being
the protocol’s own congestion control behavior, i.e.,
the window-based congestion control algorithm,
implemented by most of the AIMD-transport
protocols, introduces saw-tooth fluctuation in the
streaming rate, and the second being competing
traffic in the network. Client-side buffers can be
used for smoothing out the saw-tooth fluctuation of
a flow (Krasic, 2001). However, despite any amount
of buffering, competing traffic can have persistent
effects on the streaming rate, and consequently on
the viewing quality. The problem is more
challenging in the case of low-delay video since
client buffering is limited by end-to-end latency
limit, and also data cannot be prefetched into the
client buffer when extra bandwidth is available.
Thus streaming video applications must deal with
persistent rate changes, before the client-side buffers
are overwhelmed. The usual way is to employ
quality-adaptation control, adjusting the basic
quality-rate trade off of the video.
The primary design goal of quality-adaptation
control mechanisms is to adapt the outgoing video
stream so that, in times of network congestion, less
video data is sent into the network and consequently
fewer packets are lost and fewer frames are
discarded. This rests on the underlying assumption
that the smooth and timely play out of consecutive
frames is central to a human observer’s perception
of video quality. Although a decrease in the video
bitrate noticeably produces images of coarser
resolution, it is not nearly as detrimental to the
Prepared through collaborative participation in the
Communications and Networks Consortium sponsored by
the U.S. Army Research Laboratory under the
Collaborative Technology Alliance Program, Cooperative
Agreement DAAD19-01-2-0011.
50
Abd El Al A., Saadawi T. and Lee M. (2006).
STREAMING LOW-DELAY VIDEO OVER AIMD TRANSPORT PROTOCOLS.
In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 50-57
DOI: 10.5220/0001569700500057
Copyright
c
 SciTePress

perceived video quality as inconsistent, start-stop
play out. By switching between different quality levels
during the stream, the mechanism makes a
fundamental trade-off by increasing the video
compression in an effort to preserve a consistent
frame rate at the client.
In this paper we focus on sender-driven quality
adaptation, for low-delay video streams, to minimize
any overheads at the client. In particular, we focus
on quality adaptation using stream switching, as it
has been shown to provide better viewing quality
than adding/dropping layers, due to the layering
overhead (Cuetos, 2001). First, we introduce an
adaptive stream switching mechanism for low-delay
video that does not require either modifications to
the network transport protocol at the sender or at the
receiver, or support from the network infrastructure.
By monitoring the application buffer occupancy, the
mechanism detects the network bandwidth variations
and estimates the current and future server buffer
drain delay, and accordingly it adapts the video
transmission rate to minimize the client buffer
starvation while ensuring that the adaptation affects
the perceptual quality at the client minimally. Then,
we show that by scaling the transport protocol send-
buffer according to the available bandwidth-delay
product, the adaptation accuracy can be improved
significantly.
Although the presented mechanisms are suitable
for video transport over AIMD-based transport
protocols in general, we chose to implement them
over Stream Control Transmission Protocol (SCTP)
(Stewart, 2000). SCTP is an AIMD-based transport
protocol, with many attractive features for video
transport than TCP. The most attractive features are
multi-streaming and multi-homing support. Multi-
streaming allows data to be partitioned into multiple
streams that have the property of being
independently delivered to the application at the
receiver. This means that the loss of a data chunk
that belongs to a certain stream will only affect the
delivery within that stream, without affecting the
delivery of other streams. This feature prevents
head-of-line blocking problem that can occur in
TCP, as TCP supports only a single data stream.
Multi-homing allows a single SCTP endpoint to
support multiple IP addresses. In its current form,
SCTP multi-homing support is only for redundancy.
In addition, SCTP provides different reliability
levels, which are more suitable for video transport
than the strict reliability provided by TCP (Blak,
2002). SCTP congestion control is similar to TCP,
thus ensured to be friendly to other TCP flows
sharing the same network (Brennan, 2001).
This paper is organized as follows. In section 2, we
present related work in video rate and quality
adaptation mechanisms. In Section 3, we describe
our system architecture. Section 4 describes the
adaptation mechanisms and the SCTP send-buffer
scaling. In Section 5, we evaluate the performance
for our proposed mechanisms. Section 6 concludes
the paper.
2 RELATED WORK
Multimedia adaptation has been studied for Internet
applications, and the adaptive control schemes can
be classified into receiver-driven and sender-driven.
Receiver driven schemes allow receivers
individually to tune the received transmission
according to their needs and capabilities. Mehra and
Zakhor (Mehra, 2003) modify the TCP protocol at
the receiver end to provide video streams a nearly
CBR connection over a bandwidth limited access
link. Hsiao et al (Hsiao, 2001) present Receiver-
based Delay Control (RDC) in which receivers delay
TCP ACK packets based on router feedback to
provide constant bit rate for streaming. While
receiver buffers can be used for smoothing out rate
fluctuations, buffering is limited by the end-to-end
latency limit.
A majority of the sender-driven algorithms may
be grouped under quality adaptation schemes.
Quality adaptation techniques can further be
classified into on-the-fly encoding, adding/dropping
layers, and switching among multiple encoded
versions. Kanakia et al (Kanakia, 1993) estimate the
buffer occupancy and the service rate received by
the connection at the bottleneck queue through
periodic feedback messages from the network. These
estimates are used to control the transmission rate of
each video frame on-the-fly by adjusting the encoder
quantization factor. However, in general, on-the-fly
encoding is CPU intensive and thus regarded as
unsuitable for streaming real-time video. In the
adding/dropping layers scheme, the video stream is
partitioned into several layers using scalable coding
schemes such as MPEG-4 FGS or interframe
wavelet video encoding. Video streaming
applications can add or drop enhancement layers to
adjust the transmission rate to the available
bandwidth. In the switching-versions scheme, the
video is encoded at different rates, and therefore
different quality levels, and each of these versions is
made available to the streaming server as an
independent stream. The server detects changes in
available bandwidth and switches among the input
streams, in order to adapt the transmission rate to the
available bandwidth. Quality adaptation that is based
on multiple encoded versions has been shown to
provide better viewing quality than adding/dropping
layers, due to the layering overhead (Cuetos, 2001).
STREAMING LOW-DELAY VIDEO OVER AIMD TRANSPORT PROTOCOLS
51

Besides quality adaptation, scheduling algorithms
may also be used to improve the multimedia
streaming adaptation. Saparilla and Ross (Saparilla,
2000) prefetch future portions of the stored video
into the client buffer as bandwidth is available. For
low-delay video prefetching is not possible, and the
server application transmits the video stream at the
consumption rate, unless the available bandwidth is
less than the consumption, at which time the stream
is transmitted at the available bandwidth rate.
3 SYSTEM ARCHITERCURE
In our architecture, shown in Figure 1, low-delay
media is encoded into multiple quality streams,
which are fed to the adaptive media server. The
server selects one of these streams R
in
(t) and injects
it into the server buffer. The server buffer is drained
as fast as the network connection permits, i.e. R
out
(t).
The network output is fed into the client buffer. To
smooth out short time scale bandwidth variations
and to remove jitter, D units of time of the stream
are allowed to build up in the client prefetch buffer
before playback begins. The size of this buffer is
limited by the maximum end-to-end latency
constraints of the system. In addition, without loss of
generality, we assume that the streams are CBR
encoded.
In this paper, we present adaptation algorithms that
enable the server to stream the media across varying
network bandwidth conditions while maximizing the
video quality at the client.
4 STREAM SWITCHING
STRATEGIES
Estimation of network bandwidth in the case of
SCTP-based streaming is not straightforward since
SCTP hides the network congestion status from the
application. A somewhat delayed effect can,
however, be seen in the application buffer.
If the server is streaming at a certain rate and the
network capacity goes below this rate, this will be
reflected as increase in the server buffer occupancy.
Similarly, if the server is streaming with a rate lower
than the current network capacity, the server buffer
will start to empty.
The proposed mechanism monitors the
application buffer at the server in order to estimate
the current available bandwidth in the network, and
accordingly it adapts the streaming rate. It reacts to
the network congestion state so as to prevent client
buffer underflow (the client prefetch buffer and the
server buffer are mirror images of each other), in
addition it tries to keep the number of quality
changes at the client to a minimum so as to have
minimal effect on the user perceived quality. We
introduce below the criteria used for stepping up or
down the streaming rate. The algorithm is described
in the context of a low-delay stream, which the
server receives from a live-source and forwards to
the client over a SCTP connection.
Consider that we have N available video streams
(different encodings or derived sub-streams) with
corresponding bit-rates
j
V (
Nj ,,2,1 …=
) and we
make the decision to switch at discrete time
instances
k
t . Follows, we present the strategies to
make this decision.
Client
Network
Switching
Strategy
Streaming
Server/Proxy
Rate Control
Server
Buffer
B(t)
Adaptive
Media Pump
R
in
(t)
R
out
(t)
D
Client
Buffer
Client
Network
Switching
Strategy
Streaming
Server/Proxy
Rate Control
Server
Buffer
B(t)
Adaptive
Media Pump
R
in
(t)
R
out
(t)
D
Client
Buffer
Figure 1: Streaming system architecture.
4.1 Switching Down Strategy
The minimum delay that any incoming packet (at
time
+
k
t ) experiences with the server buffer at
measured fullness
(
)
k
tB is the time required to
drain the buffer. This delay
min
+
Δ
k
may be estimated
as
(
)
()
k
out
k
k
tR
tB
=Δ
+
min
, where
()
k
out
tR
is the output
rate estimated at time instant
k
t .
()
k
out
tR
can be
obtained using a Weighted Exponential Moving
Average (WEMA) of the past and current bandwidth
observations at the server. In order to satisfy the
client delay constraints and prevent the client buffer
from underflowing (server buffer from overflowing),
we should have
D
k
<Δ
+
min
. Hence, whenever we
observe
D
k
α >Δ
+
min
, we reduce the input rate to the
largest available rate smaller than
()
k
out
tR
, so that
we drain the server buffer build-up and preempt any
overflow. The conservative factor
α
( 10
<
<
α
), is
introduced in order to account for possible variations
in the input and output rates during sampling
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APPLICATIONS
52

interval
[ )
1
,
+kk
tt . Hence, for this interval, we select
the input rate
in
k
R
as:
()
{
}
j
tRV
Nj
in
k
VR
k
out
j
<
=
=
,,1
max
…
, (1)
where
j
V are the N available video bit-rates. The
factor
α
should be selected based on the expected
variations in the rates.
This decision strategy aggressively reduces the
input rate whenever it estimates buffer drain time as
being greater than the computed threshold. Although
such strategy follows any reductions in the available
output bandwidth rapidly, it does not take into
consideration the rate of change of the buffer, thus it
can lead to unnecessary reductions in the input rate.
For instance, even if the buffer fullness was being
steadily reduced (based on a previous switching
decision), this strategy could further reduce the input
rate. This can lead to under-utilization of available
bandwidth, and undesirable quality for the user.
Thus we combined the instantaneous decision
strategy with a look-ahead strategy that takes into
consideration the rate of change of the buffer,
through estimating the buffer fullness one sampling
interval in the future. The look-ahead strategy
estimates the server buffer at sampling instant
1+k
t
as:
( ) ( ) () ()
()
⎪
⎭
⎪
⎬
⎫
⎪
⎩
⎪
⎨
⎧
⎥
⎦
⎤
⎢
⎣
⎡
∫
−+=
+
+
0,max
ˆ
1
1
k
k
t
t
outin
kk
duuRuRtBtB
, (2)
where
()
uR
in
and
(
)
uR
out
are the instantaneous
input and output rates. If the sampling intervals are
chosen small enough, we can assume that the input
and output rates are constant over the entire interval.
Hence we may rewrite equation (2) as
() ()
(
)
()
[
]
{
}
0,max
ˆ
11 kk
out
k
in
kkk
ttRRtBtB −−+=
++
(3)
where
in
k
R and
()
(
)
k
outout
k
tRR = are the constant
input and output rates interval
[ )
1
,
+kk
tt . We then
estimate the delay
()
min
1
−
+
Δ
k
that would be experienced
at the end of this interval (before time instant
1+k
t )
as
()
()
out
k
k
k
R
tB
1
min
1
ˆ
+
+
=Δ
−
. Since during this sampling
interval we want to avoid server buffer overflow, we
would like to constrain
()
D
k
β ≤Δ
−
+
min
1
, where
β
(
10 <<
β
) is another conservative factor
introduced to account for possible variations in the
input and output rates. Combining with equation (3)
we can easily determine that:
(
)
()
out
k
kk
k
out
k
in
k
R
tt
tBRD
R +
−
−
≤
+1
β
(4)
We may thus use equation (4) to determine what
input rate to switch to such that we avoid server
buffer overflow, at the end of the current interval, as
a result of the decision. The look-ahead strategy can
avoid unnecessarily aggressive reductions and
stream switches (thereby improving the visual
quality) in the input rate by sometimes borrowing
from, and sometimes provisioning for the future.
However, it also makes assumptions that the output
rate does not change significantly over the
interval
[
)
1
,
+kk
tt . Hence, when the timescale of
network variations is smaller than the sampling
interval (i.e. the network conditions change rapidly)
the instantaneous decision is likely to outperform the
look-ahead decision, and conversely if the sampling
interval is smaller than the timescale of network
variations, the look-ahead decision is likely to
outperform the instantaneous decision.
The combined decision strategy, shown in Figure 2,
combines the benefits of these instantaneous and
look-ahead decision strategies to minimize the
number of stream switches (for better visual quality)
while following the available bandwidth accurately,
and satisfying the user delay constraints.
4.2 Switching Up Decision Strategy
While we attempt to switch down the server
streaming rate as soon as we observe a low network
bandwidth, we cannot similarly switch up the
streaming rate. This is because switching up too
rapidly can actually create congestion in the network
and thereby lead to oscillations between switching
up and switching down, adversely affecting the
video quality. Hence, it might be suitable for the
server to attempt to stream at a higher rate, only after
a certain duration during which the server does not
observe a congestion event. The problem is that
there is no explicit signal indicating when the server
should switch up. For this reason, our mechanism
carries out active experiments by probing the
network to ensure that there is enough capacity for
the next higher streaming rate. We call these
experiments,
switch-experiments. The switch
experiment
is triggered whenever the server does not
experience a congestion event for an interval
i
E
T
referred to as the
Inter-Experiment timer.
The experiment is performed by switching to the
next higher available streaming rate, such that:
{
}
j
RV
Nj
in
k
VR
in
kj 1
,,1
min
−
>
=
=
…
(5)
STREAMING LOW-DELAY VIDEO OVER AIMD TRANSPORT PROTOCOLS
53

Figure 2: Combined switching down decision strategy.
and each experiment lasts for a maximum duration
of
S
T . During the switching experiments, the
congestion monitor in the server continues to
monitor the network and if no congestion is caused
due to the experiment, this is considered an
indication that the network can support the next
higher bit rate stream. In this case, the server stays at
the higher stream. However, if congestion is
detected, as indicated in Step 3 of the combined
switch down algorithm, the sender reverts to the
lower rate. The sender also learns from the failed
switch experiment, by exponentially backing off the
Inter-Experiment timer
i
E
T for this rate, before
retrying the experiment. Backing off the timer is
likely to reduce the number of rate switches at a time
when the available bandwidth in the network cannot
support higher streaming rate. The exponential back-
off is performed as follows:
(
)
max1
,min
E
i
E
i
E
TTT γ =
+
(6)
where
max
E
T is the maximum Inter-Experiment
timer, and γ is the back-off factor. We clamp the
back-off at a maximum to guarantee the sender will
periodically probe for spare bandwidth. The
Inter-
Experiment
timer of the new stream is rested to
initial value
init
E
T , when the switch experiment to
this stream succeeds. The switching experiment
duration
S
T starts with an initial value
init
S
T and is
updated using an exponential moving average of the
time difference between starting a switching
experiment to the failure detection time. If no
congestion is detected for a duration of
S
T seconds,
then the server decides to stay at this higher
bandwidth. Otherwise the switching experiment is
terminated by switching down.
4.3 Determining the Adaptation
Parameters
The performance of our adaptation strategies is
controlled by a set of different parameters that
include the sampling interval, the buffer drain time
parameters
α and β , the switch up times
init
S
T
and
init
E
T , and the exponential back-off parameter γ.
We should keep our sampling interval small so that
we can effectively track the network bandwidth
variations. However, a small sampling interval leads
to larger overheads in system complexity and
transmitted bandwidth. In order to tradeoff these
conflicting goals, we select the sampling interval
based on the available network bandwidth; sample at
small intervals when the network bandwidth is high
(and the variations are likely to be more rapid), and
sample at larger intervals when the network
bandwidth is low (and the variations are likely to be
less frequent). We can do this by sampling every
time we transmit a fixed number of bytes.
Empirically, we have determined that sampling
every time we transmit ~16000 bytes (since we
transmit complete packets) provides a good tradeoff
for the adaptation. The other adaptation parameters
have been tuned empirically to provide a good visual
quality, however we can derive analytical bounds on
their values based on the statistical properties of the
network and video bit-rates. This is a direction of
future research.
4.4 SCTP Send-Buffer Scaling
Based on the network congestion feedback, the
SCTP sender uses the variable
CWND to estimate
the appropriate congestion window size, which
determines the maximum number of
unacknowledged packets in flight in the network at
any time (Cuetos, 2001). In addition, SCTP uses a
fixed size
send-buffer to store application data
before the data is transmitted. This buffer has two
functions. First, it handles rate mismatches between
the application sending rate and SCTP’s
transmission rate. Second, it is used to keep copies
of the packets in flight so they can be retransmitted
when needed. Since the
CWND determines the
number of in-flight packets from the
send-buffer,
setting the
send-buffer to be smaller than the CWND
would reduce the throughput of the flow. On the
other hand, setting the
send-buffer much larger than
1. Compute buffer fullness
()
k
tB and estimate
()
k
out
tR
Set
in
k
in
k
RR
1−
= .
2. Estimate
min
+
Δ
k
and
()
min
1
−
+
Δ
k
3. If D
k
α >Δ
+
min
and
()
D
k
β >Δ
+
+
min
1
Determine upper bound on input rate
()
()
⎪
⎭
⎪
⎬
⎫
⎪
⎩
⎪
⎨
⎧
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+
−
−
=
+
out
k
out
k
kk
k
out
k
in
RR
tt
tBRD
R ,max
1
max
β
Select
{}
j
RV
Nj
in
k
VR
in
j max
,,1
max
<
=
=
…
Else
Test whether we can switch up
4. Wait until next sampling interval. Goto Step 1.
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the
CWND, will cause more packets to be delayed in
the
send-buffer until they get chance to be sent in the
network when acknowledgments have been received
for the previous packets in the buffer. This will lead
the application to lose control over quality
adaptation, as it has to wait longer before making its
quality adaptation decisions. In addition, fixed
buffer size can introduce significant latency into the
SCTP stream, as the packets have to sit in the
send-
buffer
until they get chance to be sent in the network
(Goel, 2002).
We propose to dynamically adapt the
send-buffer
size to be at least
CWND packets. However, if the
send-buffer size is limited to CWND, then 1) SCTP
must inform the application when it has available
space for more packet(s), as well as when it
increases the
CWND, and the application must write
the next packet(s) before SCTP can send it. Thus,
system timing and scheduling behavior can affect
SCTP throughput. 2) Back-to-back acknowledgment
arrivals exacerbate this problem. These adverse
effects throughput, and can be reduced by adjusting
the buffer size so that it is larger than
CWND. We
selected to set the
send-buffer size to be 2 * CWND,
as shown in equation (7), which ensures that the
SCTP has a window worth of unsent data to keep the
self-clock of acknowledgments flowing (Semke,
1998).
SCTP send-buffer(t) = 2 * CWND(t) (7)
The sending application uses non-blocking write
calls to ensure that the application is not blocked
while there is no space in the SCTP buffer to accept
more data, and the data stays in the application
buffer. Thus the application buffer size will
accurately reflect the current network conditions.
5 PERFORMANCE ANALYSIS
In order to examine our proposed adaptation
strategies, we implemented them in Opnet network
simulation tool (Opnet). We used the topology
shown in Figure 4, where we assume that source S1
is representing the video server, while receiver R1
represents the video client, and S1 is using SCTP for
streaming the video to the R1. Sources S2 – Sn are
generating traffic that share the bottleneck A-B with
the video stream. Unless specified otherwise, we
assume that the bottleneck bandwidth is 5 Mbps and
the Round Trip time (
RTT) is 10 ms.
1 Mbs, 10 ms 1 Mbs, 10 ms
1 Mbs, 10 ms 1 Mbs, 10 ms
5 Mbs, 10 ms
.
.
.
.
.
.
S1
Sn
R1
Rn
AB
1 Mbs, 10 ms 1 Mbs, 10 ms
1 Mbs, 10 ms 1 Mbs, 10 ms
5 Mbs, 10 ms
.
.
.
.
.
.
S1
Sn
R1
Rn
AB
Figure 4: Simulation topology.
For the source S1, we use a XviD video codec
(Xvid) to encode a 320×240 - 15 frames per second
(fps) video sequence at different bit-rates,
j
V (512,
420, 335, 255, 210, 170 Kbps). The adaptation
mechanism parameters are: N = 6, D
= 3 sec , γ = 2,
10=
init
E
T sec, 60
max
=
E
T sec, 10=
init
S
T sec.
Additionally, we select the parameters α = 0.4, and
β = 0.5. We measure the video quality in terms of
the achieved average bit-rate, variations in the
quality at the client, and data loss due to prefetch
buffer starvation which can result in frame drops or
pauses in the video to allow for rebuffering. Hence,
we want to track the available bandwidth faithfully,
while minimizing prefetch buffer underflow and
maintaining a relatively constant quality by
minimizing the number of stream switches.
5.1 Bandwidth Adaptation
We examined the adaptation mechanisms by real
network trace, obtained from the PlanetLab
(Planetlab), between two nodes one in the US east
coast and the other in the west coast. The traces were
collected over 70 minutes with 50 competing TCP
connections and the bandwidth varied between 700
Kbps and 120 Kbps. We show the adaptation
performance over 3000 seconds in Figure 5. As
expected the instantaneous decision leads to over-
aggressive switching down, thereby not following
the network trace accurately, unlike the combined
decision strategy. However, as we have mentioned
before, bandwidth fidelity is not the only
performance metric we evaluate. We also measure
the number of stream switches and the number of
dropped packets (due to server buffer overflow).
5.2 Effect of SCTP Send-Buffer
Scaling
To examine the combined adaptation mechanism
with the SCTP
send-buffer scaling, we compared the
adaptation mechanism, with and without the
send-
buffer
scaling. To vary the bandwidth between
STREAMING LOW-DELAY VIDEO OVER AIMD TRANSPORT PROTOCOLS
55

sender S1 and receiver R1, we varied the number of
active SCTP connections, as shown in Table 1.
Figure 6 shows the bandwidth available to the SCTP
connection between S1-R1, as well as the video
stream rate received at the client R1. The graph
shows that although the adaptation mechanism
without the buffer scaling is able to track the
available bandwidth, using the buffer scaling will
We assumed that the SCTP connections will always
have data to send. For each set of SCTP streams we
run the experiment with and without the SCTP
send-
buffer
scaling. We calculated the normalized SCTP
allow the application to track the available
bandwidth more accurately, as the server buffer will
be more reflective to the SCTP available bandwidth
than the using a fixed size for the SCTP
send-buffer.
To ensure that the SCTP
send-buffer scaling will not
affect the SCTP throughput We run different number
of SCTP streams through the bottleneck A-B, and
we set all the connections to last for 200 seconds.
throughput as the average throughput of a SCTP
connection with the buffer scaling option to that
without the buffer scaling.
Figure 7 shows the normalized SCTP throughout,
with 90% confidence interval, versus the number of
active SCTP connections. The figure shows that the
SCTP buffer scaling will not have an adverse effect
on the SCTP throughput.
Table 1: Number of active SCTP connections.
Simulation Time
(Sec.)
Number of SCTP
Connections
0 25
15 20
30 40
70 20
In Figure 8, we examined the video stream
goodput as a function of the
RTT. We define the
goodput as the percent of video packets that arrive
before the display time of their video frame at the
client to the total number of video packets sent from
the server.
In all the experiments we set the client pre-
buffering to 3 seconds. Results show that without
quality adaptation 24% to 62% of the non-adaptive
stream packets will miss their deadlines at the client.
The reason behind this that the video sender is not
adapting its rate to the available bandwidth, which
leads many packets to be delayed at the sender and
to miss their deadlines. This is reflected at the client
as buffer underflow events, that leads the displayed
video to continually freezes.
6 CONCLUSIONS
We proposed server adaptation mechanism, for low-
delay video over AIMD transport protocols. The
server estimates information about the available
network bandwidth by monitoring the application
buffer and performs stream switching to meet
bandwidth and delay constraints. We investigate a
strategy that combined instantaneous and look-ahead
strategies for switching down the transmission rate,
and switch up the rate in a controlled manner after
observing periods of no congestion. We estimate the
current and future server buffer drain delay, and
derive the transmission rate to minimize client buffer
starvation. In addition, we presented a simple scaling
mechanism to the transport protocol
send-buffer that
allows the adaptation mechanism to follow
accurately the network bandwidth and reduce the
adaptation decision time. We implemented these
algorithms over SCTP. The strategy with combined
look-ahead and instantaneous decisions can follow
the network bandwidth accurately. In addition, the
proposed SCTP
send-buffer scaling does not affect
the SCTP throughput, compared to SCTP with a
fixed buffer size, while it improves the accuracy of
0 500 1000 1500 2000 2500 3000
0
200
400
600
800
1000
(a) Instantaneous Adaptation
Received B it Rate (Kbps)
Tim e (Sec.)
Network Trace
Received Rate
0 500 1000 1500 2000 2500 3000
0
200
400
600
800
1000
(b) Combined Adaptation
Bit Rate (Kbps)
Tim e (Sec.)
Network Trace
Received Rate
Figure 6: Received rate vs. available bandwidth.
SIGMAP 2006 - INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA
APPLICATIONS
56

the quality adaptation.
0 20406080100120140
20
40
60
80
100
120
140
160
180
200
220
Bandwidth
Combined Adapataion
Combined Adapation + Buffer Scaling
Bit Rate (Kbps)
Time (Sec.)
Figure 5: Adaptation performance for real trace. trace.
1 2 4 8 16 32 64 128 256
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Number of SCTP Connections
Normalized SCTP Throughput
(0.97)
(0.84)
(1.08)
(1.07)
(0.98)
(1.17)
(1.01)
(0.92)
(1.1)
(0.94)
(0.86)
(1.04)
(1.05)
(0.96)
(1.13)
(1.021)
(0.92)
(1.11)
(0.98)
(0.92)
(1.67)
Figure 7: Normalized SCTP throughput versus number of
connections.
0 100 200 300 400 500
0
20
40
60
80
100
Video Stream Goodput (%)
Round Trip Time RTT (m s)
Non-Adaptive
C om bin ed Adaptation
C om bin ed Adaptation + B uffer Scaling
Figure 8: Video stream goodput vs. round trip time.
Disclaimer
The views and conclusions in this document are
those of the authors and should not be interpreted as
representing the official policies, either expressed or
implied, of the Army Research laboratory or the
U.S. Government.
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