Artificial Intelligence Aided Joint Bit Rate Selection and Radio Resource Allocation for Adaptive Video Streaming over F-RANs

Recently, fog-computing-based radio access networks (F-RANs) have been conceptualized to provide high quality of experience (QoE) for adaptive bit rate (ABR) streaming, where additional computing capacity is supplemented on fog nodes to facilitate complicated cross-layer optimization (i.e., joint bit rate selection and radio resource allocation). However, finding an optimal global solution with acceptable complexity is still infeasible by the conventional optimization methods. In this work, we propose an artificial intelligence (AI) aided joint bit rate selection and radio resource allocation scheme referred to as iABR, which provides a new vision for handling the over-complicated optimization in F-RANs. Based on multi-agent hierarchy deep reinforcement learning, the proposed iABR can dynamically allocate radio resource and select bit rate in a multiuser scenario, by perceiving the network environment and clients' player information. Moreover, long short-term memory (LSTM) is employed by the iABR algorithm, which enables accurate prediction of the change of channel quality by learning the history of the wireless channel. Hence, iABR is able to adjust the action policy in advance to accommodate the future channel quality for avoiding bit rate fluctuation. According to the experimental results, the iABR exhibits higher QoE in terms of high average bit rate, low rebuffering ratio, and average bit rate variance. Last but not least, the QoE performance of all the clients are fairly guaranteed by the iABR algorithm, enhancing the practicality of AI-driven F-RANs for multimedia service delivery.