Optimizing video-on-demand with source coding

In order to cost-effectively serve a large number of users, video-on-demand (VoD) content providers often place distributed servers close to user pools. These servers have heterogeneous streaming and storage capacities, and collaboratively share contents with each other. A critical challenge is how to optimize movie storage and retrieval so as to minimize system deployment cost due to server streaming, server storage, and network transmission between servers. Using a general and comprehensive cost model, we propose a novel VoD architecture using linear source coding. All the movies are source-encoded once at the repository, by coding k source symbols of movie m to n(
^m
) source-coded symbols. These coded symbols are then distributed to the servers. We optimize n(
^m
) and the number of symbols to retrieve from each server for a request. Our solution approaches asymptotically to global optimum as k increases. We show that even when k is low (say, 30), near optimality can be achieved. Furthermore, the solutions on n(
^m
), symbol distribution and retrieval can be efficiently computed with a linear program (LP). Through extensive simulation, our algorithm is shown to achieve substantially the lowest cost, outperforming traditional and state-of-the-art heuristics by a significantly wide margin (by multiple times in many cases).