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Caching and rate allocation are two promising approaches to support videostreaming over wireless network. However, existing rate allocation designs donot fully exploit the advantages of the two approaches. This paper investigatesthe problem of cache-enabled QoE-driven video rate allocation problem. Weestablish a mathematical model for this problem, and point out that it isdifficult to solve the problem with traditional dynamic programming. Then wepropose a deep reinforcement learning approaches to solve it. First, we modelthe problem as a Markov decision problem. Then we present a deep Q-learningalgorithm with a special knowledge transfer process to find out effectiveallocation policy. Finally, numerical results are given to demonstrate that theproposed solution can effectively maintain high-quality user experience ofmobile user moving among small cells. We also investigate the impact ofconfiguration of critical parameters on the performance of our algorithm.

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