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