Improving Mobile Interactive Video QoE Via Two-level Online Cooperative Learning

计算机科学 强化学习 体验质量 移动设备 人工智能 实时计算 多媒体 机器学习 计算机网络 服务质量 操作系统
作者
Huanhuan Zhang,Anfu Zhou,Huadong Ma
出处
期刊:IEEE Transactions on Mobile Computing [IEEE Computer Society]
卷期号:22 (10): 5900-5917
标识
DOI:10.1109/tmc.2022.3179782
摘要

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an “offline learning” paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing Legato, an online RL framework for real-time mobile interactive video system. Legato puts many individual RL agents directly into the video system, which make video bitrate decisions in real-time and evolve their models over time. Legato then employs a two-level cooperative learning mechanism to enhance video QoE. Firstly, Legato proposes a score-based robust learning algorithm to eliminate risks of quality degradation caused by the RL model's exploration attempts. Then Legato adaptively aggregates agents following a network condition-aware manner to form its corresponding high-level RL model that can help each individual to react to unseen network conditions. We implement Legato on an interactive real-time video system. Based on the exhaustive evaluations, we find that Legato outperforms the state-of-the-art algorithms significantly across a wide range of QoE metrics.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
斯文败类应助科研通管家采纳,获得10
刚刚
MchemG应助科研通管家采纳,获得10
1秒前
英俊的铭应助科研通管家采纳,获得10
1秒前
CAOHOU应助科研通管家采纳,获得10
1秒前
1秒前
1秒前
2秒前
爱撞墙的猫完成签到,获得积分10
2秒前
小马甲应助干雅柏采纳,获得10
2秒前
小晓完成签到,获得积分10
2秒前
becky发布了新的文献求助10
3秒前
jszhoucl发布了新的文献求助10
4秒前
星期八发布了新的文献求助10
4秒前
时有落花至完成签到,获得积分10
4秒前
4秒前
无与伦比发布了新的文献求助30
8秒前
10秒前
一人独钓一江秋完成签到,获得积分10
10秒前
12秒前
13秒前
干雅柏发布了新的文献求助10
15秒前
搜集达人应助俏皮芷蕊采纳,获得10
17秒前
上官若男应助sugar采纳,获得10
18秒前
xxxllllll发布了新的文献求助30
18秒前
18秒前
CodeCraft应助wangqiuhong采纳,获得10
19秒前
21秒前
桐桐应助jszhoucl采纳,获得10
21秒前
黄健斌完成签到,获得积分10
22秒前
HarryChan完成签到,获得积分10
24秒前
27秒前
28秒前
28秒前
华仔应助小绵羊采纳,获得10
30秒前
Andema发布了新的文献求助10
31秒前
俏皮芷蕊发布了新的文献求助10
32秒前
33秒前
xiao_niu完成签到,获得积分10
33秒前
liu发布了新的文献求助10
34秒前
大模型应助墨水采纳,获得10
35秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989444
求助须知:如何正确求助?哪些是违规求助? 3531531
关于积分的说明 11254250
捐赠科研通 3270191
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174