A Comparison of Layering and Stream Replication Video Multicast Schemes

Taehyun Kim and Mostafa H. Ammar

Content

Research Goal Replication VS Layering Experimental Comparison Results Conclusion

Research Goal

A systematic comparison of video multicasting schemes designed to deal with heterogeneous receivers– Replicated streams– Cumulative layering– Non-cumulative layering

Stream Replication

Multiple video streams Same content with different data rates Receiver subscribes to only one stream Example

– SureStream of RealNetworks– Intelligent streaming of Microsoft

Replicated Stream Multicast

R1, R2 and R3 are from different domain

Receivers subscribe to only one stream

R1 joins the high quality stream (8.5Mbps)

R2 receives the medium quality stream (1.37Mbps)

R3 joins the low quality stream (128kbps)

Cumulative Layering

1 base layer + enhancement layers Base layer

– Independently decoded Enhancement layer

– Decoded with lower layers– Improve the video quality

Example– MPEG-2 scalability modes

Non-Cumulative Layering

Video is encoded in two or more independent layers

Receiver can join any subset of the video layer without joining the layer 1 multicast group

Example– Multiple description coding (MDC)

Layered Video Multicast

R1 subscribes to all video layers (10 Mbps)

R2 joins enhancement layers 1 and the base layer (1.5 Mbps)

R3 just receives the base layer (128kbps)

Layering or Replication?

Common wisdom states:– “Layering is better than replication”

However, it depends on– Layering bandwidth penalty– Specifics of encoding– Protocol complexity– Topological placement of receivers

Layered Video Multicast

Considering 20% overhead, the data rates contributing to the video quality are 8Mbps, 1.2Mbps and 102.4Kbps

Stream Replication: video quality are 8.5Mbps, 1.37Mbps and 128kbps

Bandwidth Penalty

Information theoretic results– Recent results showed that the performance of layered codi

ng is not better than that of non-layered coding– Increase the number of layers => significant quality degrada

tion

Packetization overhead– Enhancement layers carry:

Picture header GoP information Macroblock information

Experimental Comparison

Non-layered streams has better video quality

Difference in data rates ranges from 0.4% at 27.7dB PSNR to 117% at 23.2dB PSNR

For a good quality video, the overhead is around 20%

Providing a Fair Comparison

Need to insure that each scheme is optimal Two dimensions

– Stream assignment algorithm Determine the reception rate of each receiver by aggregating

the data rates of the assigned streams– Rate allocation algorithm

Determine the data rate of each stream Goal

– Maximize the bandwidth utilization by each scheme for a given network a particular set of receivers and given available bandwidth on the network links

System Model

Model the network by a graph G = (V, E)– V is a set of routers and hosts– E is a set of edges representing connection links

receivers ofnumber theisn

,...,1,| niVccC ii

Isolated rate– The reception rate of the receiver if there is no

constraint from other receivers in the same session

Stream Assignment

Cumulative layering– Define

i is the data rate of a stream and m is the number of layers

– Assign as many layers as possible Compute the isolated rates Assign that does not exceed the isolated rate

miRii ,...,1,|

i

Stream Assignment

Stream replication– Define

i is the data rate of a replicated stream and m is the number of replicated streams

Set of receivers assigned to stream i, – Two objectives

Minimum reception rate for all receivers is greater than zero Maximum

Greedy algorithm– Allocate 1 to all receivers to satisfy the minimum reception rate constraint

– Receiver is assigned a stream that has not been assigned and has the maximum value of group size and stream rate product

miRii ,...,1,|

ijj cc )(|

ji ii

m

i i bZje

subject to

1

Stream Assignment

Non-cumulative layering– Define

i is the data rate of a non-cumulatively layered stream and m is the number of streams

Set of receivers assigned to stream i, – Two objectives

Minimum reception rate for all receivers is greater than zero Maximum

miRii ,...,1,|

)(|'jij cc

ji ii

m

i i bZje

subject to

1

'

Rate Allocation

Cumulative layering– Optimal receiver partitioning algorithm (Yang, Kim and Lam 2000)

determines the optimal rates of layer i, i Receivers are partitioned into K groups (G1, G2,…, GK) Objective is to maximize the sum of receiver utilities Dynamic programming algorithm is used to find an optimal partition For a given partition, an optimal group transmission rate can be

determined Stream replication

– Stream rates, i, are allocated based on the optimal cumulative layering rate

i

2j j

1

mi2

1i

i

Rate Allocation

Non-cumulative layering– Receiver can subscribe to any subset of layers

without joining the base layer ={1,2,4} => isolated rates of {1,2,3,4,5,6,7}– 2m-1 different link capacities with m non-

cumulative layers i are allocated based on i =>

43213

32121

212

11

Performance Metrics

Average reception rate– Average rate received by a receiver

Average effective reception rate– Amount of data received less the layering overhead

Total bandwidth usage– Adding the total traffic carried by all links in the network for

the multicast session

Efficiency– total effective reception rate / total bandwidth usage

Network Topology

Georgia Tech Internetwork Topology Models (GT-ITM)

– 1 server– 1640 nodes with 10 transit domains– 4 nodes per transit domains, 4 stubs per transit node, 10 no

des in a stub domain– transit-to-transit edges = 2.4Gbps – stub-to-stub edges = 10Mbps and 1.5Mbps – transit-to-stub edges = 155Mbps, 45Mbps and 1.5Mbps – number of layers = 8– amount of penalty = 20%

Date Reception Rate

Cumulative layering can receive more data

Number of layers in cumulative layering is twice as many as that of non-cumulative layering

Cumulative

Non-cumulative

Replication

Bandwidth Usage

Bandwidth consumption of cumulatively layered multicasting is the largest

Cumulative

Non-cumulative

Replication

Effective Reception Rate

Only 80% of data contributes to improving the video quality

Cumulative

Non-cumulative

Replication

Efficiency

Replicated stream video multicasting is more efficient

Cumulative

Non-cumulative

Replication

Effect of Overhead

Layering overhead of more than 7% tends to favor the replicated stream approach

Effect of the number of layers

Efficiency of stream replication is always greater than that of cumulative layering

The effect is not so significant

Narrow Distribution

Wide distribution Narrow distribution

The layering approach achieves better bandwidth efficiency when multiple streams share the bottleneck link

In narrow distribution, the reception rates in Figure (a) is larger than that of Figure (b) by 1.63Mbps

Efficiency

Compared to the wide distribution results, the performance of replicated stream video multicast is degraded

Cumulative

Non-cumulative

Replication

Protocol Complexity

Receiver-driven Layered Multicast (RLM) Receivers decide whether to drop additional layer or not Join experiment incur a bandwidth overhead Receivers send a join message and multicast a message

identifying the experimental layer to the group

Layered video multicasting– Receiver can join multiple groups– Large multicast group size

Replicated stream video multicasting– Receiver only join one group– Small multicast group size

Average Group Size

Group size in cumulatively layered video multicasting is twice as large as that in stream replication

More bandwidth to multicast a message reporting the “join” experiment

Conclusion

Identified the factors affecting relative merits of layering versus replication

– Layering penalty– Specifics of the encoding– Protocol complexity– Topological placement

Developed stream assignment and rate allocation algorithms

Investigated the conditions under which each scheme is superior