Name
Title | ||
|---|---|---|
Deep Reinforcement Learning-Based Rate Adaptation For Adaptive 360-Degree Video Streaming |
Abstract | ||
|---|---|---|
In this paper, we propose a deep reinforcement learning (DRL)-based rate adaptation algorithm for adaptive 360 degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to reduce the possible switching latency of the field of view (FoV), we design a new QoE metric by introducing a penalty term for the large buffer occupancy. A scalable FoV method is further proposed to alleviate the combinatorial explosion of the action space in the DRL formulation. Then, we model the rate adaptation logic as a Markov decision process and employ the DRL-based algorithm to dynamically learn the optimal video transmission rate. Simulation results show the superior performance of the proposed algorithm compared to the existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683779 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
360-degree videos, adaptive streaming, rate adaptation, deep reinforcement learning | Field of view,Mathematical optimization,Latency (engineering),Computer science,Markov decision process,Real-time computing,Quality of experience,Video quality,Combinatorial explosion,Scalability,Reinforcement learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuowen Kan | 1 | 2 | 2.48 |
| J. Zou | 2 | 203 | 35.51 |
| Kexin Tang | 3 | 2 | 1.80 |
| Chenglin Li | 4 | 116 | 17.93 |
| Ning Liu | 5 | 88 | 31.20 |
| Hongkai Xiong | 6 | 22 | 8.85 |