MPC2[jls-end-space/]: A novel MPC-based cache-aware adaptive video streaming over HTTP

Edge computing can potentially improve user Quality of Experience (QoE) in ever-increasing video streaming by caching popular videos. However, most state-of-the-art client-side ABR algorithms, such as MPC and Pensieve, ignore the presence of a cache on the video delivery path, leading to unsatisfying video QoE. Thus, in this paper, we present MPC2[jls-end-space/], a novel cache-aware ABR algorithm for cache-based adaptive video streaming, which can optimally combine throughput, buffer occupancy, and cache state based on model predictive control (MPC). Facing the problem of stale bandwidth measurement, MPC2 proposes a bandwidth interpolation scheme based on the features of edge-assisted video streaming, introducing no additional overhead. Moreover, MPC2 further proposes Bitrate Combination Search algorithm based tabu search (BCS-tabu) to resolve the practical deployment issues, which greatly reduces the computation delay. To evaluate the performance of MPC2[jls-end-space/], we compare MPC2 with the related state-of-the-art works using trace-driven simulation under various real-world traces. The results demonstrate that MPC2 outperforms the best state-of-the-art work, a deep reinforcement learning-based cache-aware ABR, achieving superior optimality and stability in cache-based video streaming systems.