Bucket-Filling: An Asymptotically Optimal Video-on-Demand Network With Source Coding

Abstract

There has recently been growing interest for content providers to provide video-on-demand (VoD) as a cloud service. In such a network, the content provider may rent heterogeneous resources (such as streaming and storage capacities ) from geographically distributed data centers deployed close to user pools. These data centers (or proxy servers) collaboratively share content with each other to serve their local users. A critical challenge is to optimize movie storage and retrieval to minimize the deployment cost consisting of streaming, storage, and network transmission between data centers. We propose a novel and effective movie storage and retrieval using linear source coding. All the movies are source-encoded once at the repository, by taking every $q$ source symbols of movie $m$ to generate ${n^{(m)}}$ coded symbols. These coded symbols are then distributed to the servers in the cloud. Based on a general and comprehensive cost model, we optimize ${n^{(m)}}$ and the number of symbols to retrieve from remote servers for a local movie request. The optimal solution can be efficiently computed with a linear programming (LP) formulation . Our solution is proved to asymptotically approach the global minimum cost as $q$ increases. Even when $q$ is low, near optimality can be achieved. To accommodate large movie pool and system parameter changes, we propose algorithms for movie grouping and on-line re-optimization which significantly reduce the computational complexity with little compromise on optimality. Through extensive simulation, our algorithm is shown to achieve the lowest cost, outperforming traditional and state-of-the-art heuristics with a substantially wide margin.