指令预取
计算机科学
架空(工程)
强化学习
一般化
排队
人工智能
下载
体验质量
计算机网络
实时计算
服务质量
数学
操作系统
隐藏物
数学分析
作者
Chenglei Wu,Zhi Wang,Lifeng Sun
出处
期刊:Network and Operating System Support for Digital Audio and Video
日期:2021-07-02
被引量:5
标识
DOI:10.1145/3458306.3460995
摘要
Conventional tile-based 360° video streaming methods, including deep reinforcement learning (DRL) based, ignore the interactive nature of 360° video streaming and download tiles following fixed sequential orders, thus failing to respond to the user's head motion changes. We show that these existing solutions suffer from either the prefetch accuracy or the playback stability drop. Furthermore, these methods are constrained to serve only one fixed streaming preference, causing extra training overhead and the lack of generalization on unseen preferences. In this paper, we propose a dual-queue streaming framework, with accuracy and stability purposes respectively, to enable the DRL agent to determine and change the tile download order without incurring overhead. We also design a preference-aware DRL algorithm to incentivize the agent to learn preference-dependent ABR decisions efficiently. Compared with state-of-the-art DRL baselines, our method not only significantly improves the streaming quality, e.g., increasing the average streaming quality by 13.6% on a public dataset, but also demonstrates better performance and generalization under dynamic preferences, e.g., an average quality improvement of 19.9% on unseen preferences.
科研通智能强力驱动
Strongly Powered by AbleSci AI