1. Proxy Cache Management for Fine-Grained Scalable Video Streaming Jiangchuan Liu, Xiaowen Chu, and Jianliang Xu INFOCOM 2004

2. Outline System model Minimizing transmission cost Client utility (trading off backbone bandwidth) Performance evaluation Single video Multiple videos Compared with stream replication

3. System model A server that stores a repository of videos. A set of proxies on the edge of the network. Intercept client requests. Cache some portions of selected videos. If a request is issued, The cached portion of the video is delivered by the proxy to the client. The uncached portion will be fetched from the server.

4. Functionalities of the video server and a proxy

5. Definitions (1/2) M : classes of clients (the clients in the same class has the same bandwidth) c i : client bandwidth of class i ( c 1  c 2  …  c M ) b i : streaming rate to any client of class i ( b i  c i )  i : utility of a client in class i (  i = b i / c i ) p i : probability that a client is in class i : client request rate for the video

6. Definitions (2/2) L : the length of the video (L = L t + L s ) L t : the length of prefix L s : the length of suffix p ET : probability of early termination (only access the prefix) 0 < p ET < 1 r base : the rate of the base layer (CBR) H : proxy cache size for the video : volume of the video with rate c M, : backbone bandwidth consumption

7. Lemma (1/2) H is less than the size of the base layer -> just cache the base layer H is larger than the size of the base layer.

8. Lemma (2/2) r t ≥ r base r t ≥ r s cache prefix (H t ) cached suffix suffixprefix rate r base rtrt cMcM rsrs cache prefix (H t ) cached suffix suffixprefix rate r base rtrt cMcM rsrs

9. Different portions of a FGS video stream To be fetched from server

10. Backbone bandwidth consumption The optimal solution:

11. Trading off backbone bandwidth with client utility To reduce bandwidth consumption, we can block some of the client requests; assign lower streaming rate to clients. (preferred) Goal : reduce to  : the factor of backbone bandwidth reduction

12. The utility optimization problem  i-1 c i-1   i c i : to preserve the priority of the client classes in resource sharing B H : minimum backbone bandwidth consumption Maximize total client utility

13. Proxy management for multiple heterogeneous videos N : number of videos H T : total cache size  T : total bandwidth reduction factor

14. Configurations (1/2) M = 5, c 1 = 128 Kbps, c i = 2c i-1 (p 1, p 2, …, p 5 ) Uniform : (0.2, 0.2, 0.2, 0.2, 0.2) S-narrow : (0.5, 0.2, 0.15, 0.1, 0.05) S-wide : (0.05, 0.1, 0.15, 0.2, 0.5) L = 100 minutes, L t = 20 minutes p ET = 0.3 Default cache grain : Default bandwidth grain :

15. Configurations (2/2) MaxLen : cache the base layer MaxRate : cache all layers

16. Performance evaluation Single video Multiple videos Compared with stream replication based caching

17. Backbone bandwidth reductions (uniform class)

18. Backbone bandwidth reductions (S-narrow class)

19. Backbone bandwidth reductions (S-wide class)

20. Discuss If most of clients have low client bandwidth, caching the lower layers in advance can be efficient. Otherwise, caching all layers is usually preferred. However, this result highly depends on the values of parameters and should not be considered as a good strategy. So the optimal solution proposed by the paper is important.

21. Expected client utility (uniform class) ()()

22. Expected client utility (S- narrow class) ()()

23. Expected client utility (S-wide class) ()()

24. Discuss For the S-narrow class distribution, a relatively high volume is cached for the stream to a narrowband client, so the expected utility is still high.

25. Performance gaps (max gap) max gap: (utility) compared with and

26. Performance evaluation Single video Multiple videos Compared with stream replication based caching

27. Multiple videos (N = ?) A baseline scheme with a uniform cache partition ( H (k) = H T / N ) among the videos, a bandwidth partitioning proportional to the client population of each video, the utility assignment for each video optimized using the algorithm for MU-SV. Cache Optimal employing the optimal cache partition alone (i.e., the bandwidth partition is the same as the baseline). Band Optimal employing the optimal bandwidth partition alone (i.e., the cache partition is the same as the baseline).

28. System utility improvement (uniform class)

29. System utility improvement (S-narrow class)

30. System utility improvement (S-wide class)

31. Performance evaluation Single video Multiple videos Compared with stream replication based caching

32. FGS based caching v.s stream replication based caching (1/2)

33. FGS based caching v.s stream replication based caching (2/2)