Optimal Transcoding and Caching for Adaptive Streaming in Media Cloud: an Analytical Approach

Nowadays, large-scale video distribution feeds a significant fraction of the global Internet traffic. However, existing content delivery networks may not be cost efficient enough to distribute adaptive video streaming, mainly due to the lack of orchestration on storage, computing, and bandwidth resources. In this paper, we leverage Media Cloud to deliver on-demand adaptive video streaming services, where those resources can be dynamically scheduled in an on-demand fashion. Our objective is to minimize the total operational cost by optimally orchestrating multiple resources. Specifically, we formulate an optimization problem, by examining a three-way tradeoff between the caching, transcoding, and bandwidth costs, at each edge server. Then, we adopt a two-step approach to analytically derive the closed-form solution of the optimal transcoding configuration and caching space allocation, respectively, for every edge server. Finally, we verify our solution throughout extensive simulations. The results indicate that our approach achieves significant cost savings compared with the existing methods used in content delivery networks. In addition, we also find the optimal strategy and its benefits can be affected by a list of system parameters, including the unit cost of different resources, the hop distance to the origin server, the Zipf parameter of users' request patterns, and the settings of different bitrate versions for one segment.