Bandit Learning-Based Edge Caching for 360-Degree Video Streaming With Switching Cost

Virtual Reality (VR) and Augmented Reality (AR) applications are expected to be a key driver for the 5G network. The need of the MEC support for caching and transcoding of omnidirectional content has been identified as a major topic of research. In this paper, we study the optimal caching problem in an MEC-enabled VR system for tile-based viewport-adaptive 360-degree video streaming. For content caching, the replacement of the cached content inevitably incurs content switching cost, which is largely ignored in caching policy design. In this work, we aim to find the optimal caching policy to optimize the long-term transmission quality by considering both transmission latency and content switching cost. We apply the combinatorial multi-armed bandit (CMAB) theory to solve the problem with no a-priori knowledge on content popularity. Moreover, the CUCB with switching cost (CUCBSC) algorithm is adopted in this scenario. A transformation mechanism is designed to transform the generated single period optimization (SPO) problem into a multiple choice knapsack problem (MCKP). Rigorous theoretical analysis on the performance of the proposed algorithm is also provided. Finally, the effectiveness of the proposed learning based caching policy is confirmed by simulation results in terms of learning, hit-ratio, transmission and content switching delay.