User Intent-Oriented Video QoE with Emotion Detection Networking

With the ever-growing number of users enjoying online video service in mobile environments, video streaming services have been dominating the mobile traffic. It can be predicted that a small improvement in the user's watching experience will cause a substantial leap in profitability in terms of content providers and distributors, network operators and service providers for mobile videos. Though recent years have witnessed effective efforts to improve a user's video quality of experience (QoE) by the use of big data for analyzing users' viewing behaviors based on large-scale, video- viewing history datasets, it is very challenging to precisely analyze users' hidden intents and feelings when they are watching online videos. In addition to obtain a better video QoE, we propose to introduce user's emotional reactions into QoE assessment. In this scheme, first, the user's mood is detected in a real time fashion via emotion detection networking. Then, a mood matching process is performed to gain the similarity of the user's intent and the video content property in terms of emotion design. Finally, a novel, decision tree-based adjustment model is proposed to characterize the relationship between QoE and various factors, including buffer ratio, average bitrate, and the user's emotions. Our study opens a road for improving video QoE based on emotion detection networking.