ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (2): 418-426.doi: 10.7544/issn1000-1239.2021.20190759

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  1. 1(电子科技大学计算机科学与工程学院 成都 611731);2(电子科技大学航空航天学院 成都 611731) (
  • 出版日期: 2021-02-01
  • 基金资助: 

Estimating QoE for OTT Video Service Through XDR Data Analysis

Huang Lisheng1, Ran Jinye1, Luo Jing1, Zhang Xiangyin2   

  1. 1(School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731);2(School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731)
  • Online: 2021-02-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2018YFB0804505) and the Science and Technology Project of State Grid Corporation of China (522722180007).

摘要: 互联网电视(over the top, OTT)视频业务逐渐成为最流行的在线业务之一,然而网络视频往往由于网络质量差、服务平台过载等原因,出现播放失败、卡顿次数增加、缓冲时间过长等质量问题,导致用户感知质量(quality of experience, QoE)下降.因此,运营商需要精确评估和掌握用户在使用网络视频业务过程中的质量体验,以便提前发现质量问题,进一步开展网络和业务优化工作.为了解决该问题,提出一种基于用户呼叫/事务/会话记录数据(extend data record, XDR)的无参考网络视频质量评估方法.该方法从大量XDR数据中提取出与视频质量相关性高的少量信息,将大规模、低价值的XDR话单数据转化为高价值、小规模的视频质量特征信息,有利于后续人工智能算法的应用和视频业务质量评价,降低进一步数据挖掘的资源成本,提升机器学习的输入样本质量和QoE评价结果的准确性.实验表明:使用该方法提取后的数据进行QoE预测,得到的预测结果在准确性方面明显优于目前基于原始XDR数据的QoE机器学习评估方法.

关键词: 视频质量评估, 用户呼叫/事务/会话记录数据, 数据分析, 互联网电视, 用户感知质量

Abstract: Over the top (OTT) video services have gradually become one of the most popular online services. However, due to poor network quality, overload of service platform and other reasons, OTT services often encounter quality problems such as playback failure, increased number of stuck, and long buffer time, which lead to the decline of quality of experience (QoE). Internet service providers need to accurately evaluate the QoE of OTT video services so as to identify quality problems in advance and further optimize networks and services. In this paper, a no-reference OTT video quality estimation method based on extended data record (XDR) data analysis is proposed. It extracts a small amount of information with high relevance to video quality from a large amount of XDR data, and converts large-scale, low-value XDR data into high-value and small-scale video quality feature information. This method facilitates the application of subsequent artificial intelligence algorithms for OTT video quality evaluation, reduces the cost of further data mining, and improves the accuracy of machine learning model and QoE evaluation results. The data extracted by this method is used for OTT video QoE prediction, and experimental results show that the accuracy of the QoE prediction results is significantly better than that of the current evaluation results using the original XDR data.

Key words: video quality estimation, XDR data, data analysis, OTT(over the top), QoE(quality of experience)