UAV-Assisted Real-Time Video Transmission for Vehicles: A Soft Actor–Critic DRL Approach
Dan Wu, Liming Wang, Meiyan Liang, Yunpeng Kang, Qi Jiao, Yajun Cheng, Jian Li
Abstract
With the rapid development of Internet of Things (IoT) and mobile communication technology, Internet of Vehicles (IoV) has potential prospects in providing live streaming services and improving the Quality of Experience (QoE) of ground mobile vehicles (GMVs). However, when the communication link is blocked in emergency situations, such as road congestion and base station (BS) paralysis, it is still an arduous task to provide real-time high-definition HD video for vehicle users. Fortunately, as the aerial mobile BS, unmanned aerial vehicle (UAV) is expected to be deployed for emergency communication recovery in extreme scenarios. But high mobility of GMVs and UAV leads to dynamic changes of network topology, how to establish a stable communication link between UAV and GMVs to ensure the QoE of vehicle users has become a huge challenge. Therefore, this study proposes a novel live video transmission scheme based on scalable video coding (SVC). By jointly optimizing the UAV trajectory, video layer selection, and bandwidth resource allocation, our objective is to maximize long-term QoE while reducing UAV’s flight energy consumption. To maximize QoE, time delay and bitrate switching at GMVs’ receiver are set as penalty items while maximizing the reward of video quality. The joint optimization problem is modeled as an Markov decision process (MDP), and is solved by the soft actor–critic (SAC) deep reinforcement learning (DRL) algorithm, which is based on the maximum entropy framework. Extensive simulation results based on real-world data sets show that compared with other DRL algorithms, the proposed scheme has excellent performance in all respects.