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

TY - JOUR

T1 - MPC2[jls-end-space/]

T2 - A novel MPC-based cache-aware adaptive video streaming over HTTP

AU - Wu, Haiqiao

AU - Xiao, Yuming

AU - Zhang, Chen

AU - Gong, Peng

AU - Wu, Dapeng Oliver

N1 - Publisher Copyright: © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

PY - 2026/5

Y1 - 2026/5

N2 - 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.

AB - 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.

KW - Bitrate adaptation

KW - Cache-based video streaming

KW - Model predictive control

UR - https://www.scopus.com/pages/publications/105035261792

U2 - 10.1016/j.jnca.2026.104448

DO - 10.1016/j.jnca.2026.104448

M3 - Article

AN - SCOPUS:105035261792

SN - 1084-8045

VL - 249

JO - Journal of Network and Computer Applications

JF - Journal of Network and Computer Applications

M1 - 104448

ER -