  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.
