Machine Learning based User QoE Evaluation for Video Streaming over Mobile Network

Quality of experience (QoE) serves as a direct evaluation of users' experience in mobile video transmission and is critical to ensure good network service. Although many efforts have been made to predict QoE based on network parameters of the user terminal equipment, it is difficult to predict QoE based on Quality of Service (QoS) offered by the network servers. In this paper, a machine learning based QoE evaluation method is proposed to evaluate user QoE in real-time by analyzing the QoS characteristics for mobile video transmission. For this purpose, we construct a large-scale dataset by collecting more than 300 thousand pieces of metrics data with two kinds of key quality indicators (KQIs) describing the QoE and 91 key performance indicators (KPIs) describing the QoS. A two-process feature subset selection (FSS) method consisting of single parameter pre-FSS and multi-parameter FSS is then proposed to find the KPIs related to KQIs. An Extra-Trees model is finally developed to learn the relationships between the KPIs and KQIs. By employing machine learning and data analytics on network data with the data-driven framework, the proposed method can predict the user QoE according to the QoS of network servers. The results prove that our proposed method can outperform other state-of-the-art approaches.