Cooperative Layered Wireless Video Multicast Ozgu Alay, Thanasis Korakis, Yao Wang, Elza Erkip, Shivendra Panwar

Introduction Video multicast over wireless channels  Wireless video applications are emerging  Multicast is effective  Wireless video multicast is still a challenging problem High packet loss rate Bandwidth variations Cooperation is a natural solution  Higher spatial diversity  Adaptive to network conditions

Prior Work: Cooperation for Unicast physical-layer cooperation for point-to-point video communication  Single-layer cooperation  layered cooperation MAC-layer cooperation for point-to-point communication

Each receiver has different channel quality Conventional Multicast  Source transmits based on furthermost receiver  the receivers with a good channel quality unnecessarily suffer and see a lower quality video. Why Cooperative Multicasting ?

Cooperative Multicast  Divide all the receivers into two groups such that receivers in Group 1 have better average channel quality than Group 2  Sender targets receivers with good channel quality (Group1)  These receivers relay the video to other receivers (Group2)  It is likely that we achieve a larger coverage area (Extended Group 2).  Both groups see better quality Relay 1 Relay 4AP Relay 3 Relay 2 Group 1 Group 2 Extended Group 2

Received Video Rates T1T1 T2T2 T2T2 T

Design Variables Number of relays N Sustainable rates (R 1, R 2 ) or transmission ranges (r 1, r 2 ) Time partition (T 1, T 2 ) N controls the tradeoff between R 2 and T 2 How to optimize?  Maximize the average quality  All users have same quality  Group1 has better quality

Approach For a particular (r 1, r 2 ) we determine the optimum (T 1, T 2 ) and N in two steps. 1. We first determine the user partition with a minimum number of relays. 2. Then for this user partition, we find the optimum T 1 and T 2 (time scheduling) that maximizes the system performance index By repeating the above procedure for all possible (r 1, r 2 ) we find the optimum user partition and time scheduling that maximizes the performance criterion.

User Partition Goal: Find minimum number of relays N that covers all the users User partition is defined by (r 1, r 2 ) and the separation angle  where, N = 2  /2 

User Partition We define  max as the maximum angle which satisfies the constraints below,

Optimum User Partition  is maximum when Then, using cosine theorem

Optimum User Partition Then N is, And r ext can be computed as

Time Scheduling and Performance Metric We use exhaustive search over a discretizied space of feasible T 1 and T 2, for each candidate T 1 and T 2, determine Rv 1 and Rv 2 and correspondingly D 1 and D 2. Here D 1 (Rv 1 ) is the distortion of Group 1 receivers and D 2 (Rv 2 ) is the distortion for Group 2 receivers.

Minimum Average Distortion N 1 and N 2 are the number of users in Group 1 and Group 2, respectively.

Equal Distortion at all users We require all the receivers have the same distortion. In other words, we find the optimum user partition and time scheduling that minimizes D 1 (Rv 1 ) = D 2 (Rv 2 ).

Best Quality at Group 1 users Considering that relays are spending their own resources to help others, We find the optimum user partition and time scheduling that minimizes D 1 (Rv 1 ) while guaranteeing Rv 2 =  R d

Sustainable Rates vs. Distance with IEEE b r1=61m, R1=11 Mbps r2=72m, R2=5.5 Mbps r3=100m, R3=1 Mbps

Example Scenario b based WLAN Uniformly distributed users within 100m radius (r=100m) Achievable rate with direct transmission to all users, R d = 1 Mbps  =0.75 Soccer  704x576 resolution  240 frames

Performance

Visual Quality 750 kbps ( dB ) Mbps ( dB ) 3.75 Mbps ( dB )

Conclusion User cooperation can improve the quality of service in video multicast  Equal quality at all users  Better quality at selected users  All better than direct transmission Optimization of relay selection, user partition, and transmission scheduling depends on the chosen multicast performance criterion