.png){kind=icon}
返回
QUVE: QoE Maximizing Framework for Video-Streaming
.png){kind=icon}
PRE
AI
DOI：10.1109/JSTSP.2016.2632060.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As video-streaming services for mobile terminals are becoming more popular, the volume of mobile traffic is growing rapidly. To deliver traffic-heavy network services without degrading users' overall experience, one must examine service characteristics and end-to-end network conditions. In this paper, we propose QUVE, a framework for maximizing the user's quality of experience (QoE) of video streaming services. The QUVE framework consists of two key components: a QoE estimation model and QoE parameter estimation method. The QoE-estimation model is based on the rebuffering count and time, and content-encoding conditions. The QoE parameter-estimation method estimates forthcoming network quality and the corresponding rebuffering count and time that the user will experience. The effectiveness of this framework was demonstrated through a large-scale field trial for Niconico video service, one of the most popular video-streaming services in Japan. We gathered more than 1.4 billion pieces of feedback data from the in-service trial and found that our framework enhances user QoE by selecting the best encoding conditions suited for user network conditions.
Keyword:
Quality of experience
video streaming service
QoE maximization
QoE estimation
network quality estimation
rebuffering condition estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.9K
被引数:
1.1W
机构
暂无机构信息
引用论文
Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem.png)
Complexity
OAAI
ComplexityIF0
OAAI分享收藏
分享收藏Water Oxidation by Mononuclear Ruthenium Complex with a Pentadentate Isoquinoline–Bipyridyl Ligand
PREAI
分享收藏
查看更多内容