Reinforcing Edge-DASH: Deep Learning for Multi-Objective Streaming Optimization

Arash Bozorgchenani, David Naseh, Daniele Tarchi, Sergio A. Salinas Monroy, Farshad Mashhadi, Qiang Ni

TL;DR: This work considers an Edge-DASH scenario and forms a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation, and employs deep reinforcement learning for three of them and develops a heuristic solution for the fourth.

With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation. Due to the complexity of the joint problem, we decompose it into sub-problems and address them separately. To solve the resulting sub-problems, we employ deep reinforcement learning, specifically the Deep Deterministic Policy Gradient (DDPG) method, for three of them, and develop a heuristic solution for the fourth. Simulation results demonstrate that our approach enhances performance across multiple metrics, including improved video delivery, reduced buffer underflow and overflow, and more efficient caching, which collectively enable greater utilization of edge resources for streaming. Moreover, we evaluated inference latency across edge and cloud hardware, confirming sub- to few-millisecond performance suitable for real-time deployment. This showcases the benefits of combining learning-based and heuristic techniques to meet the growing demand for adaptive video streaming in edge computing environments.