When Machine Learning Algorithms Meet User Engagement Parameters to Predict Video QoE

In recent years, there has been a substantial increase in the distribution of videos over the Internet, and this has become one of the major activities that attract extensive attention. This means that users expect to watch videos of the highest quality. Quality of experience (QoE) describes the degree of satisfaction or annoyance of a user when they are using a multimedia service or application. Meeting users’ expectations requires understanding the factors that influence QoE and efficiently managing resources to optimize video quality. The current objective approaches that assess QoE mostly rely on the analysis of video traffic. However, recent research has demonstrated that this approach cannot sufficiently evaluate perceived QoE and that multiple factors, including media technical features, influence QoE. It is crucial for service providers to identify the effects of social context, in addition to those of user-related, content-related, and system factors, on perceived QoE of the end user. Recent studies have focused on understanding the characteristics of user behavior and engagement, as well as the effect of these factors on QoE. In this study, we use social context factors and user engagement as subjective factors to structure a user QoE evaluation model. First, we study social context factors and user engagement characteristics and investigate their correlation with QoE. Next, we build a metric that estimates the end-to-end QoE for a specific aspect of user actions. Then, by simulating mathematical metrics, we employ machine learning models to predict QoE; finally, we validate this approach using metrics for statistical evaluation of quality prediction models.