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

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

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

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

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} – 2 m -1 different link capacities with m non- cumulative layers –  i are allocated based on  i =>

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 nodes 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