Paper # – 2009 A Comparison of Heterogeneous Video Multicast schemes: Layered encoding or Stream Replication Authors: Taehyun Kim and Mostafa H. Ammar Presented by: Koushik Ananthasayanam Varun Kulkarni 1

Overview ~ Aim of the research paper Comparison between Replication and Layering Experiments based on the Comparisons Results Conclusion 2

What this paper aims at ? A structured and systematic comparison of video multicasting schemes. Only those schemes that deal with the heterogeneous receivers. Replicated Streams. Cumulative layering. Non- cumulative Layering. 3

Aim (Contd.) ‘Layered multicast transmission is superior to the replicated stream multicasting’ – widely believed. Authors contradict this dogma – bandwidth overhead which is incurred by encoding video stream in layers, cannot be neglected while comparison. 4

Replicated Streams ~ More than one video streams. Replicated – same contents but with different data rates. However, receiver subscribes to only one suitable stream. Examples: SureStream by RealNetworks. Intelligent Streaming by Microsoft. 5

Replicated Streams (Contd.) 6

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) 7

Cumulative Layering ~ Video can encoded in a base layer and one or more enhancement layers. Base Layer: Independently decoded. Enhancement Layer(s): Decoded with lower layers to improve the video quality. Layer ‘k’ can be only be decoded along with layers 1 to k-1. Example: MPEG-2 scalability modes. 8

Non- Cumulative Layering ~ Video is encoded in two or more independent layers. Two or more independently decoded layers. Receivers select any subset of video layer and join it, without joining the layer-1 multicast group. Eg: Multiple Description Coding. 9

Layered Multicast (Contd.) 10

Layered Multicast (Contd.) 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) 11

Layering or Replication ? Common belief: ‘Layering is better than replication.’ - Really ? Bandwidth overhead in layering. Cater to specifics of encoding. Implicit Protocol Complexity Topological placement of receivers 12

Layering or Replication ? (Contd.) 13

Layering or Replication (Contd.) Assuming 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 14

Overhead in Layered Video ~ Information theoretic results: 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 and Macroblock information. Protocol Overhead: Receivers need to manage the multiple subscriptions in layered video. 15

Experimental Evidence ~ Non-layered streams has better video quality The layering overhead ranges from 0.4% at 27.7dB PSNR to 117% at 23.2dB PSNR For a good quality video, the overhead is around 20% 16

A Fair Comparison ~ In order to have a meaningful comparison, need to ensure that each scheme is optimal. 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 17

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. n is number of receivers Isolated rate: The reception rate of the receiver if there is no constraint from other receivers in the same session 18

Stream Assignment Cumulative Layering: Given stream rates α i - Assign as many layers as possible: Compute the isolated rates Assign Σ i α i that does not exceed the isolated rate. 19

Stream Assignment (Contd.) Stream replication ◦ Define δ = {δ i | δ i ε R +, i =1,…,m}  δ 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 as much as possible. Greedy algorithm ◦ Allocate δ 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 ◦ Receiver can either subscribe to base or any other high quality layer. 20

Stream Assignment (Contd.) 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 as much as possible. 21

Stream Assignment Algorithm for Replicated Stream Multicasting 22 A receiver can subscribe to either the base layer stream or high quality stream

Stream Assignment Algorithm for Non-cumulatively layered multicasting 23 A Receiver can subscribe to multiple streams. The data rate of the aggregated streams leads to the minimum distortion.

Rate Allocation Cumulative layering ◦ Optimal receiver partitioning algorithm 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. ◦ 1 is the stream rate of the base. If a receiver can join up to k layers, the receiver has the capability to join a replicated stream of data rate  k. 24

Rate Allocation (Contd.) Non-cumulative layering ◦ Receiver can subscribe to any subset of layers without joining the base layer ◦ = data rate of non-cumulatively layered stream. ◦ Given non-cumulative layered stream  ={1,2,4} => selective subscription: 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 => 25

Experiments ( 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 26

Network Model 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% 27

Experiment Results Random Receiver Distribution - Reception Rate: Cumulative layering can receive more data Number of layers in cumulative layering is twice as many as that of non- cumulative layering 28

Experiment Results Random Receiver Distribution - Effective Reception Rate: Stream replication has the highest effective reception rate. 29

Experiment Results Random Receiver Distribution - Total Bandwidth usage: Cumulative layering has the highest Total Bandwidth Usage. 30

Experiment Results Random Receiver Distribution - Bandwidth usage efficiency: Stream Replication has the highest Bandwidth usage Efficiency. 31

Experiment Results Clustered receiver distribution. 32

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 33

Experimental Results Average number of groups and average groups size. 34

Experiment Results The receivers are randomly distributed. The group size in cumulatively layered video multicasting is twice as large as that in stream replication. Layered multicasting requires more bandwidth. 35

Experiment Results (Contd.) Receiver in a cumulatively layered video multicast session requires more buffer size and better synchronization capability than replicated stream video multicasting Receiver in cumulative layering subscribes to more than five layers on average whereas a receiver in stream replication subscribes to only one stream 36

Conclusion The Paper has identified the factors affecting relative merits of layering versus replication ◦ Layering penalty ◦ Specifics of the encoding ◦ Protocol complexity ◦ Topological placement It has developed stream assignment and rate allocation algorithms And Investigated the conditions under which each scheme is superior Paper has given a new comparison approach towards video multicast streams 37

Our Comments! The paper brings up an unbiased support for stream replication approach. The people supporting only Layered stream multicast approach should re- think. The paper concludes the support for stream replicating approach based on specific scenarios. More generalization in experimental scenarios is essential to strengthen the specified support. 38

Questions ?? 39