DRRT-PPO: dynamic resource-aware robust transcoding via proximal policy optimization for mobile edge video streaming

Mobile Edge Computing (MEC) offers a promising solution to reduce latency in video streaming by bringing compute and storage closer to end users. However, dynamic content popularity, multi-bitrate demand, and limited edge resources pose significant challenges for joint transcoding and resource allocation. In this paper, we formulate the transcoding decision and resource-scheduling problem as a Markov Decision Process whose state encodes bandwidth and compute availability, request context, and workload features. We design a composite reward that balances end-to-end delay, backhaul bandwidth use, and compute cost. Building on this formulation, we propose DRRT-PPO, a single-agent Proximal Policy Optimization framework that jointly decides when to transcode and how to allocate bandwidth and CPU resources. By leveraging prioritized experience replay, entropy-regularized exploration, and clipped policy updates, DRRT-PPO learns robust, adaptive strategies under fluctuating workloads. Extensive simulations with Zipf-distributed requests demonstrate that DRRT-PPO reduces average latency by 6.4% compared to Greedy, No-Transcoding, and heuristic baselines, while achieving lower backhaul usage, minimal compute cost, high cache hit rates, and low timeout probabilities. These results confirm DRRT-PPO’s effectiveness for efficient and reliable edge-based video delivery.