Cache-Enabled Dynamic Rate Allocation via Deep Self-Transfer Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning method with self-transfer to optimize cache-enabled video rate allocation, significantly improving user experience in wireless networks.

Contribution

It presents a novel deep Q-learning approach with knowledge transfer for cache-enabled QoE-driven video rate allocation, addressing limitations of traditional methods.

Findings

Effective maintenance of high-quality user experience in mobile scenarios

Deep RL approach outperforms traditional dynamic programming methods

Parameter configuration impacts algorithm performance

Abstract

Caching and rate allocation are two promising approaches to support video streaming over wireless network. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled QoE-driven video rate allocation problem. We establish a mathematical model for this problem, and point out that it is difficult to solve the problem with traditional dynamic programming. Then we propose a deep reinforcement learning approaches to solve it. First, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience of mobile user moving among small cells. We also investigate the impact of configuration of critical parameters on the performance of our algorithm.