Multi-Timescale Actor-Critic Learning for Computing Resource Management With Semi-Markov Renewal Process Mobility

This paper studies artificial intelligence (AI) aided communication and computing resource allocation in a vehicular network that supports blockchain-enabled video streaming. Our study aims to improve the operating efficiency and to maximize the transcoding rewards for blockchain based vehicular networks. Our resource allocation policy considers the vehicular mobility, which is modelled with a highly-realistic Semi-Markov renewal process, as well as the real-time video service delay constraints. We propose a multi-timescale actor-critic-reinforcement learning framework to tackle these grand challenges. We also develop a prediction model for the vehicular mobility by using analysis and classical machine learning, which alleviates the heavy signaling and computation overheads due to the vehicular movement. A mobility-aware reward estimation for the large timescale model is then proposed to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the developed theoretical findings in this paper and the significant performance gains due to our proposed multi-timescale framework.