Online Orchestration of Collaborative Caching for Multi-Bitrate Videos in Edge Computing

In the traditional video streaming service provisioning paradigm, users typically request video contents through nearby Content Delivery Network (CDN) server(s). However, because of the uncertain wide area networks delays, the (remote) users usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers caching infrastructures in closer proximity to end users than conventional Content Delivery Networks (CDNs). Yet, for video caching, MEC's potential has not been fully unleashed as it overlooks the opportunities of collaborative caching and multi-bitrate video transcoding. In this paper, we model and formulate an Integer Linear Program (ILP) to capture the long-term cost minimization problem for caching videos at MEC, allowing joint exploitation of MEC with CDN and real-time video transcoding to satisfy arbitrary user demands. While this problem is intractable and couples the caching decisions for adjacent time slots, we design a polynomial-time online orchestration framework which first relaxes and carefully decomposes the problem into a series of subproblems solvable in each individual time slot and then converts the fractional solutions into integers without violating constraints. We have formally proved a parameterized-constant competitive ratio as the performance guarantee for our approach, and also conducted extensive evaluations to confirm its superior practical performance. Simulation results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 13.6% improvement on average in terms of total cost.