Estimating QoE for OTT Video Service Through XDR Data Analysis

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.