Content Driven and Reinforcement Learning Based Resource Allocation Scheme in Vehicular Network

Lots of videos are transmitted for content understanding tasks in vehicular networks, which puts pressure on limited communication resources. Traditional resource allocation schemes do not consider video content, so the performance of content understanding tasks performed on the transmitted videos is not optimal. To solve this problem, in this paper, we proposed a video frames priority driven and reinforcement Q-learning based resource allocation scheme. First, we propose a video frames content priority evaluation method from the perspective of video content, and the evaluation results are the basis of resource allocation. Then, we propose a Q-learning based resource allocation scheme in vehicular network scenario, which provides more reliable resources for video frames with higher priority. Finally, experiments on real datasets validate that the proposed scheme can help to improve the performance of video content understanding tasks, such as traffic accident detection tasks.