Adaptive Video Streaming for Massive MIMO Networks via Novel Approximate MDP

The scheduling of downlink video streaming in a massive multiple-input-multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations. Each video consists of a sequence of segments, which can be transmitted to the requesting users with variable video bitrates. To facilitate adaptive video streaming, a number of physical-layer frames are grouped as a super frame. We formulate the adaptation of transmitted segment number, frame allocation and segment bitrate in all the super frames as an infinite-horizon Markov decision process (MDP), whose objective is a discounted measurement of the average Quality-of-Experience (QoE). A novel approximate MDP method is proposed to obtain a low-complexity scheduling policy. Specifically, a baseline policy is introduced and its asymptotic value function is derived analytically. The low-complexity scheduling policy will be obtained from one-step iteration based on the analytical expression, which becomes a performance lower bound on the derived policy. It is shown by simulations that the proposed low-complexity scheduling policy has significant performance gain over the baseline policy.