Video Transmission Over Varying
Bandwidth Links
MTP Final Stage Presentation
By: Laxmikant Patil
Under Guidance of
Prof. Sridhar Iyer
Presentation Outline
• Introduction & Motivation
• Problem Definition
• Related Work
• Traffic Pattern based Adaptive Multimedia
Multicast (TPAMM) Architecture
• Solution Strategy
• Simulation & Results
• Conclusion
• References
Introduction & Motivation
Key Terms

Playout Rate: The rate at which video is shown at client

Delay Tolerant Applications: Clients can tolerate some delay
before playout starts
e.g. DEP offering live courses to remote students, Live
concert streaming, MNCs training employees across cities

Startup Latency: Maximum duration of time client is ready to
wait before playout starts
Introduction & Motivation (Contd…)
Need for Adaptive Mechanisms


Heterogeneity of receivers capabilities
o
Transmission capabilities
o
Displaying capabilities
S
Heterogeneity of receivers requirements
o
Delay tolerance values
o
Minimum acceptable quality
84 kbps
R1
80 kbps
C1

75 kbps
R2
= 20
70 kbps
C2

= 40
80 kbps
C3 
= 30
Introduction & Motivation (Contd…)
3 ways to transfer data from source to client
1.
Streaming solution
S
Stream at rate ai
C
ai is bottleneck b/w,
Time= L
2.
Partial download
S
Encoding rate ai = ?
C
ai is avg. b/w for TX
Time= L + startup_latency ?
ai = Base encoding rate
3.
Complete download
S
Play
Time= L + Download duration
C
Problem Definition
•
“Objective is to use to overcome the problem of variations in link
bandwidth and provide consistent video quality to the client.”
•
We propose to use startup latency and prediction model based
approach to overcome this
Example
110
100
90
80
70
60
50
40
30
20
10
0
Given:
•
Startup latency = 5 min
•
Length of video L = 60 min
aavg = ?
10
0
90
80
70
60
50
40
30
20
•
10
0
Bandwidth (kbps)
S-C
Time (min)
 L
 ai
0
L
60 5
50
 Aavg
 (100  x)dx
0
60

 (50 x)dx
50
60
 70.833
Related work
•
[SAMM] Multilayering: Video is encoded as base layer and
enhancement layers.

Client receive number of layers depending on their capabilities

Objective is to decide number of layers & encoding rates of each
layer
•
[KRTCR] Transcoding : Changes the encoding rate of the video file to
desired rate

Transcoding only at source

Transcoding at relay nodes
•
[AIMA] Buffer-based adaptation: uses occupancy of buffer on
transmission path as a measure of congestion
•
[AVMI] Simulcast: Source maintains different quality stream and
receiver switches across streams. Combination of single-rate multicast
and multiple-unicast.
TPAMM Architecture
(Traffic Pattern based Adaptive Multimedia Multicast)
Solution strategy
• Single hop topology
• Multi hop topology
• Multicast tree topology
• Prediction window & offset computation
Single hop topology
• Find
 L
 ai
0
L
 Aavg
S
C
Single hop topology (Contd…)
•
Need to find “Critical points” during transmission
C
S
 L
 ai
0
L
 Aavg
Single hop topology (Contd…)
No Critical points
(Accumulated Bw) >= (Consumed Bw)
Critical points : at t =100 sec
(Accumulated Bw) < (Consumed Bw)
Multi hop topology (Source-Relay-Client Scenario)
R
110
100
90
80
70
60
50
40
30
20
10
0
C
Extra b/w but not useful
deficit b/w at link R-C
Compensate b/w
S-R
R-C
Time (min)
100
90
80
70
60
50
40
30
20
10
Effective deficit
0
Bandwidth (kbps)
S
Multihop scenario
S
R1
R2
Rn
C
Multicast Tree Topology
S
84 kbps
R1
80 kbps
C1

75 kbps
R2
= 20
70 kbps
C2

= 40
80 kbps
C3 
= 30
Prediction Window & Time-Offset Computation
Prediction window
• Startup latency
• Duration of video
• All predictions values
Last Prediction window
Encoding rate
per interval
•
We modify algorithm to work for prediction window size, by computing time-offset.
•
Startup latency for next window = Current Startup latency + time-offset
•
Duration of video for next window = Current duration of video - time-offset
Prediction Window & Time-Offset Computation
(Contd…)
Prediction window
•
•
Last Prediction window
Following values are known

Encoding rate for current feedback interval (e.g. 60 kbps)

Transmission rate for current feedback interval (e.g. 90 kbps)

Feedback interval duration (e.g. 10 sec)
Actual_playout_duration_Tx (A) is computed as
(Encoding rate / Transmission rate ) * Feedback interval duration
•
=15 sec
Expected_playout_duration_Tx (E) is computed as
(current_playout_time)
* Feedback interval duration = 10 sec
(current_playout_time + current_startup_latency)
•
Time-offset = (Actual_playout_duration_Tx) – (Expected_playout_duration_Tx)
•
Time-offset for this example is 5 sec.
Simulation & Results
• Effect of Delay Tolerance on Encoding Rate
• As Delay Tolerance
increases Encoding Rate
also increases
Simulation & Results (Contd…)
•
Effect of Prediction Window size on Video Quality
• Parameter: Standard
deviation of encoding rate
• As prediction window
size increases, variations
in video quality are
reduced.
•With small increase in
prediction window size,
there is significant drop in
variation.
Simulation & Results (Contd…)
•
Effect of Prediction Window size on Video Quality
• As prediction window size increases, variations in video quality are reduced.
Simulation & Results (Contd…)
•
Maximize Minimum Video Quality During Playout
• Minimum Video Quality throughout playout is maximized in TPAMM scheme.
Conclusion
•
We have introduced a class of algorithms known as
Traffic Pattern based Adaptive Multimedia Multicast
(TPAMM) algorithms.
•
In TPAMM scheme abrupt link bandwidth variations
are not reflected at client side, ensuring good user
perceived video quality.
•
TPAMM scheme maximizes the minimum video
quality during playout.
References
1.
[SAMM] Brett Vickers, Albuquerque and Tatsuya Suda, Source-
adaptive multi-layered multicast algorithm for real-time video
distribution. IEEE/ACM Transactions on Networking, 8(6):720-733,
2000.
2.
[AVMI] Jiangchuan Liu, Bo Li and Ya-Qin Zhang. Adaptive video
multicast over the internet. IEEE Multimedia, 10(1):22-33,2003.
3.
[KRTCR] Rajeev Kumar, JS Rao, AK Turuk, S. Chattopadhyay and
GK Rao A protocol to support Qos for multimedia traffic over
internet with transcoding www.ee.iastate.edu/~gmani/tiw2002/internet-qos.pdf
4.
[AIMA] X. Wang and H. Schulzrinne. Comparison of adaptive
internet multimedia applications. In IEICE Trans. COMMUN. 1999.