Rate Allocation for Peer-to-Peer Video Conferencing using Layered Video

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1 Rate Allocation for Peer-to-Peer Video Conferencing using Layered Video Eymen Kurdoglu Prof. Yao Wang - Video Lab Prof. Yong Liu - Networked ystems Lab

2 Multi-Party Video Conferencing 1. Bandwidth demanding (around 1 Mbps) 2. trict delay requirements ( < 300 ms) 3. Heterogeneous users w.r.t. uplinks / downlinks 4. Might have entangled viewing relations 5. Might be time-varying

3 Overlay Network Design 1. erver-based solutions end AV streams to servers (Google+, kype) Might cause large delays if users far away Infrastructure + maintenance costly Privacy not guaranteed U >> U j, j=1,2,3,

4 Overlay Network Design 2. Application-level multicast: P2P solutions Users send AV to each other Can exploit user locality Avoids server costs Preserve privacy

5 Layered Video Encoding Deliver multiple quality versions separately? Layered (scalable video coding) Layer quality is function of spatial, temporal and amplitude resolutions (TAR) If less receive/display capabilities, fewer layers, less quality Disadvantage: coding overhead

6 Distribution Trees Packets delivered along a tree t rooted at source s Definition: tree rate Tree packing with continuous rates Multicast rate = sum of tree rates z x x x y y z z x x y y y z z

7 Distribution Trees Which set of trees is optimal and why? Topology: Complete graph (full connectivity) Type: Node-capacitated Constraints: Uplink R max =min ( U, 1 N U + X n2n U n + N 1 N X h2h U h!) Mutualcast trees Mutualcast: An Efficient Mechanism for One-to-Many Content Distribution, Li, Chou, et. al, ACM igcomm, 2005

8 Distribution Trees Main idea: Tree rates proportional to upload capacities. Example: 3 3 U 1 /

9 Distribution Trees Main idea: Tree rates proportional to upload capacities. Example: 3 3 U 2 /

10 Distribution Trees Main idea: Tree rates proportional to upload capacities. Example: 3 U 3 /

11 Distribution Trees Main idea: Tree rates proportional to upload capacities. Example: (U (U 1 +U 2 +U 3 )/2) / What if we have download capacities as well?

12 4 Major Problems to Examine Considering upload + download capacities ingle ource, ingle-rate ingle ource, Multi-rate Multi-ource, ingle-rate Multi-ource, Multi-rate

13 1. ingle-rate ingle-ource Case traightforward extension for R- Example: h D 3 max =min ( U, 1 N U + X n2n D 1 D 2 U n + N 1 N X h2h min(u h,nd h )!, min n2n D n )

14 2. ingle-rate Multi-ource Case ame setting, multi-source max x m s X n2n (N 1)U n X s.t. (N 1)x n n + X 8n 2 N m2n r2n\{n} x m n Mutualcast trees still optimal! (x r n +(N 2)x n r ) apple U n Distributed solutions based on Kelly s famous primal and dual framework Utility Maximization in P2P ystems, Chen, Chou, et al., ACM igmetrics, 2008

15 3. Multi-rate ingle-ource Case Allow nodes to receive the same video at different rates 1: Base layer, 2: Base + 1 st enh. layer Keep using Mutualcast trees, but with different number of branches

16 3. Multi-rate ingle-ource Case max Q = s.t. i = NX n=1 f( n ) 0 X k=1 j2n k x N 1 k j + y N k+1 A i apple D i,i2{1,...,n} ix (N k) x N k j apple U i, 8i k=1 Centralized solution is a variant of waterfilling

17 3. Multi-rate ingle-ource Case max Q = 1,..., N s.t. NX n=1 f( n ) Received Rate 0 i apple D i, 8i NX 2 3 n apple R n= User Index Layer 1 Layer 2 Layer 3 Layer 4 Rate Bound Received Rate

18 4. Multi-rate Multi-ource Case We can generalize to multi-source Each source delivers a layered video to its recipients Maximize system-wide video qualities

19 Conclusion First step to bring together the studies of layered coding and P2P networking in the multi-party video conferencing. For success, we must handle the heterogeneity of the peers. Mutualcast trees introduce minimal delay. Future work: Decentralized solutions, grouping of peers, considering discrete set of available rates, etc