In this paper, a quality of experience (QoE)-based neural live streaming method with dynamic adaptive video quality control is developed to improve streaming performance. First, the dynamic adaptive streaming issue is formulated as a Markov decision process (MDP) problem. Second, an reinforcement learning (RL)-based approach is proposed as an appropriate solution, where the client functions as an RL agent and the environment is made up of various networks. User QoE is the reward by mutual consideration of video quality and playback state. Finally, to optimize the total reward, the RL algorithm chooses the required video quality for each video segment. Experimental results show that the proposed RL-based streaming algorithm outperforms state-of-the-art schemes in terms of both temporal and visual QoE metrics by a noticeable margin while guaranteeing application-level fairness when multiple clients share a bottlenecked network. The code is available on the following website: https://github.com/OpenCode007/ICME2021.
|Title of host publication||Proceedings of the 2021 IEEE International Conference on Multimedia and Expo|
|Number of pages||6|
|Publication status||Published - 2021|
|Event||2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China|
Duration: 5 Jul 2021 → 9 Jul 2021
|Conference||2021 IEEE International Conference on Multimedia and Expo, ICME 2021|
|Period||5/07/21 → 9/07/21|
Bibliographical noteThis work was supported in part by the General Program of National Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0790, the Fundamental Research Funds for the Central Universities under Grant 2020CDJ-LHZZ-052, the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS201905, the Human Resources and Social Security Bureau project of Chongqing under Grant cx2020073 and the Suzhou Institute of USTC under Grant H20201528.
- live streaming
- quality of experience
- reinforcement learning