Soft Actor–Critic DRL for Live Transcoding and Streaming in Vehicular Fog-Computing-Enabled IoV

With the rapid development of automotive industry and telecommunication technologies, live streaming services in the Internet of Vehicles (IoV) play an even more crucial role in vehicular infotainment systems. However, it is a big challenge to provide a high quality, low latency, and low bitrate variance live streaming service for vehicles due to the dynamic properties of wireless resources and channels of IoV. To solve this challenge, we propose a novel live video transcoding and streaming scheme that maximizes the video bitrate and decreases time-delays and bitrate variations in vehicular fog-computing (VFC)-enabled IoV, by jointly optimizing vehicle scheduling, bitrate selection, and computational/spectrum resource allocation. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels of IoV. A soft actor–critic deep reinforcement learning (DRL) algorithm that is based on the maximum entropy framework, is subsequently utilized to solve the above MDP. Extensive simulation results based on the data set of the real world show that compared to other baseline algorithms, the proposed scheme can effectively improve video quality while decreasing latency and bitrate variations, and access excellent performance in terms of learning speed and stability.