Recommendation Based Video Caching and Transcoding in Mobile Edge Networks

Although edge caching assisted adaptive bitrate (ABR) video streaming has significantly reduced amount of video data to be transmitted via backhaul link, its performance is severely limited by the caching capacity and user heterogeneous preferences. To address this problem, this article investigates a collaborative recommendation, caching and transcoding approach to boost caching performance. A caching hit ratio maximization problem is formulated by designing recommendation, caching and transcoding simultaneously. The problem is an integer non-linear programming (INLP) problem and is NP-hard. To solve it effectively, we decompose it into three subproblems, i.e., content recommendation, caching placement and transcoding decision subproblems. The optimal solution to the first subproblem is derived theoretically, the second is proven to be a monotone submodular maximization problem, which is solved by greedy algorithm, and the third is a knapsack problem and is solved by dynamic programming approach. Finally, a joint recommendation, caching and transcoding (JRCT) algorithm is established to solve these three subproblems iteratively. Extensive simulations are conducted on a real world viewing dataset. It is demonstrated that JRCT can achieve higher caching hit ratio and traffic offloading ratio than the state-of-the-art baselines.