Building a Large Dataset for Model-based QoE Prediction in the Mobile Environment

Abstract

The tremendous growth in video services, specially in the context of mobile usage, creates new challenges for network service providers: How to enhance the user's Quality of Experience (QoE) in dynamic wireless networks (UMTS, HSPA, LTE/LTE-A). The network operators use different methods to predict the user's QoE. Generally to predict the user's QoE, methods are based on collecting subjective QoE scores given by users. Basically, these approaches need a large dataset to predict a good perceived quality of the service. In this paper, we setup an experimental test based on crowdsourcing approach and we build a large dataset in order to predict the user's QoE in mobile environment in term of Mean Opinion Score (MOS). The main objective of this study is to measure the individual/global impact of QoE Influence Factors (QoE IFs) in a real environment. Based on the collective dataset, we perform 5 testing scenarios to compare 2 estimation methods (SVM and ANFIS) to study the impact of the number of the considered parameters on the estimation. It became clear that using more parameters without any weighing mechanisms can produce bad results.