Video Transmission Over Varying Bandwidth Links MTP Final Stage Presentation By: Laxmikant Patil Under Guidance of Prof. Sridhar Iyer.

Video Transmission Over Varying Bandwidth Links

MTP Final Stage Presentation

By: Laxmikant PatilUnder 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

Heterogeneity of receivers requirements

o Delay tolerance values

o Minimum acceptable quality

= 30

S

R1

C1 R2

C2 C3

= 20

= 40

84 kbps

80 kbps

70 kbps

75 kbps

80 kbps

Introduction & Motivation (Contd…)

3 ways to transfer data from source to client

1. Streaming solution

2. Partial download

3. Complete downloadCS

ai = Base encoding rate

Time= L + Download duration

Play

S CStream at rate ai

ai is bottleneck b/w,

Time= L

S CEncoding rate ai = ?

ai is avg. b/w for TX

Time= L + startup_latency ?

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

833.7060

)50(

60

)100(560

50

50

0

dxxdxx

Given:• Startup latency = 5 min• Length of video L = 60 min

• aavg = ?

AavgL

aiL

0

0102030405060708090

100110

Time (min)

Ban

dwid

th (

kbps

)

S-C

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 S C

AavgL

aiL

0

Single hop topology (Contd…)

• Need to find “Critical points” during transmission

S C

AavgL

aiL

0

Single hop topology (Contd…)

Critical points : at t =100 sec

(Accumulated Bw) < (Consumed Bw)No Critical points

(Accumulated Bw) >= (Consumed Bw)

Multi hop topology (Source-Relay-Client Scenario)

0102030405060708090

100110

0

10

20

30

40

50

60

70

80

90

10

0Time (min)

Ban

dw

idth

(kbps)

S-R

R-C

Extra b/w but not useful

deficit b/w at link R-C

Compensate b/w

Effective deficit

S R C

Multihop scenario

S R1 R2 Rn C

Multicast Tree Topology

= 30

S

R1

C1 R2

C2 C3

= 20

= 40

84 kbps

80 kbps

70 kbps

75 kbps

80 kbps

Prediction Window & Time-Offset Computation

• Startup latency • Duration of video Encoding rate• All predictions values per interval

Prediction window

• 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

Last Prediction window

Prediction Window & Time-Offset Computation (Contd…)

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.

Last Prediction window

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/tiw-2002/internet-

qos.pdf

4. [AIMA] X. Wang and H. Schulzrinne. Comparison of adaptive

internet multimedia applications. In IEICE Trans. COMMUN. 1999.