Peripheral Sensing: Monitoring Quality of Experience for Video Services Based on Mobile Terminals

Abstract

Quality of experience (QoE), which directly relates to both technical evolvement and profit promotion, is a vital concern for mobile video services. However, the wireless network operators have long been troubled by the problem of lacking effective QoE monitoring approaches since the traditional evaluation methods of communication quality are objective metrics oriented. Considering that mobile terminals are the network elements closest to users, it is promising to realize a real-time QoE estimation for video services by fully utilizing the sensing capabilities of mobile terminals. As the first step, we specifically develop a mobile video testing application. With support from China Unicom, one of the three major wireless network operators in China, over 80 000 data records are collected under the real-world conditions. The collected data consist of four types of subjective scores and 13 objective parameters concerning video attributes, network performance, device capability, playback events, and external factors. After preprocessing the data set through correlation analysis, we establish the two QoE estimation models based on the C4.5 method and the gradient boosting decision tree (GBDT) method, respectively. The experimental results demonstrate that the proposed models can achieve remarkable estimation performances and outperform the baseline models. Specifically, the overall estimation accuracy of the GBDT-based model is approximately 80% for a five-level scale and approaches 90% when a more practical 3-level scale is adopted. Finally, we comprehensively discuss the estimation performances based on characteristics of the data and validate the feasibility of estimating QoE based on mobile terminals-the “peripheral sensors” of the mobile networks.

Keywords

• Decision tree
• mobile terminals
• estimation model
• QoE
• video services