PsyQoE: Improving Quality-of-Experience Assessment With Psychological Effects in Video Streaming

As media streaming grows, service providers have to focus more on users’ quality of experience (QoE). Among the metrics that influence QoE, network-oriented and media-oriented factors have been the primary concern of most existing work. Apart from the two factors, however, another crucial aspect of QoE is the user. Though the analysis and modeling of users is a turning point in the transition from QoS to QoE, the impact of users’ memory and cognitive-psychological effects have not been fully explored. In this work, we analyze, validate and quantify the relationship between multiple cognitive effects and the QoE of users in media streaming. We propose PsyQoE, a new QoE assessment framework that uniquely focuses on user perceptions during media sessions. Besides providing real-time QoE assessments through leveraging machine learning, PsyQoE also predicts the overall QoE by taking into account user biases and long-term effects. Additionally, we have designed a further modified version to cater to the needs of hardware and energy-restricted scenarios. When compared with other existing methods, our approach demonstrates enhanced performance by boosting accuracy by 7% to 16%. It also provides better interpretability and broader applicability.