Live Traffic Video Multicasting Services in UAV-Assisted Intelligent Transport Systems: A Multiactor Attention Critic Approach

Abstract

Live traffic video is vitally important for vehicles in future intelligent transport systems (ITS). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this paper, we propose a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs) assisted ITS, in which UAVs are considered as the eyes in the sky to capture real-time traffic videos. We aim to maximize the long-term video quality received by vehicles by jointly optimizing vehicle grouping and spectrum allocation. Considering the interactions among UAVs, the above optimization problem is formulated as a multi-agent coordination problem in the form of a Markov game (MG). The MG is subsequently solved by leveraging a state-of-the-art multi-agent deep reinforcement learning (MADRL) algorithm, namely, multi-actor attention critic (MAAC), in which an attention mechanism is utilized to pay attention to other agents to make the learning process more effective and scalable. Extensive simulation results show that the MAAC-based algorithm has better performance in terms of video quality and spectrum efficiency compared with the baseline methods.