PAINT: Partial in-network transcoding for adaptive streaming in information centric network

Abstract

Information centric network (ICN) has emerged as a promising architecture to efficiently distribute content over the future Internet. However, ICN proposals may still not be cost efficient enough for adaptive video streaming. The problem is, each ICN node caches duplicated copies of the same content for each bitrate version in its limited storage space. Thus the cache hit ratio drops, and the bandwidth cost of serving the cache missed requests increases. This paper proposes PAINT (Partial In-Network Transcoding) scheme to reduce the operational cost of delivering adaptive video streaming over ICN. Specifically, we consider both the in-network caching and transcoding services at each ICN node, where the storage and transcoding resources can be dynamically scheduled. Then we formulate an optimization problem to balance the trade-off between the transcoding and bandwidth costs. Next we analytically derive the optimal strategy, and quantify cost savings compared with existing schemes. Finally, we verify our solution by intensive numerical evaluations. The results indicate PAINT can achieve significant cost savings (e.g., up to 50% in typical scenarios). Besides, we find the optimal strategy and the cost savings can be affected by the cache capacity, the unit price ratio, the hop distance to origin server, and the Zipf parameter of users' request patterns.