UAV-assisted Online Video Downloading in Vehicular Networks: A Reinforcement Learning Approach

Online video becomes a significant service in daily life, and it usually adopts a caching and playing mechanism. Due to high mobility and changeable topology, challenges of video downloading still exist in vehicular networks, especially in areas where the roadside units (RSUs) are not fully covered. The flexible deployment of the unmanned aerial vehicle (UAVs) compensate for the lack of RSU coverage, and thus this paper considers that a cyclic flight UAV to assist RSUs in providing video download services for vehicles. With the help of UAV, seamless communication coverage and stable transmission links ensure better service quality for vehicles. In addition, we propose a model-free algorithm based on a deep Q network to find the optimal UAV decision policy to achieve the minimized stalling time. Finally, the simulation results are given to demonstrate that the proposed solution can effectively maintain a high-quality user experience.