ISSN 1000-1239 CN 11-1777/TP

### 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).

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.

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