Adaptive Video Streaming for Massive MIMO Networks via Novel Approximate MDP

Lan，Qiao1; Lv，Bojie1; Wang，Rui1; Gong，Yi1; Huang，Kaibin2

10.1109/ICC40277.2020.9148822

2020-06-01

1550-3607

978-1-7281-5090-1

IEEE International Conference on Communications

2020-June

1-7

7-11 June 2020

Dublin, Ireland

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.

Streaming media MIMO communication Downlink Quality of experience Scheduling Bit rate Resource management

第一

英语

EI

20203409063833

Communication channels (information theory) ; Complex networks ; MIMO systems ; Network layers ; Scheduling ; Iterative methods ; Quality of service ; Video streaming

Information Theory and Signal Processing:716.1 ; Computer Systems and Equipment:722 ; Computer Software, Data Handling and Applications:723 ; Management:912.2 ; Numerical Methods:921.6 ; Probability Theory:922.1

2-s2.0-85089433366

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.University of Hong Kong,Hong Kong,Hong Kong

Lan，Qiao,Lv，Bojie,Wang，Rui,et al. Adaptive Video Streaming for Massive MIMO Networks via Novel Approximate MDP[C],2020:1-7.