Implementation of Quality of Experience Prediction Framework through Mobile Network Data

Generally, a reliable method of analyzing the quality of experience is through the subjective method, which is time consuming, lacks usability, lacks repeatability in real-time and near real-time. Another method is the objective measurement that aims at predicting the subjective measurement based on the estimated mean opinion score. Therefore, this study adopted the objective measurement by implementing a quality of experience framework, which employed predictive analytics techniques to analyze the mobile internet user experience dataset gathered through the mobile network. The predictive analytics employed the use of multiple regression, neural network, decision trees, random forest, and decision forest to predict the mobile internet perceived quality of experience. Result from the study shows that decision forests performs better than other algorithms used for the predictive analytics. In addition, the result indicates that the predictive analytics can be used to enhance the allocation of network resources based on location and time constituted in the dataset

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

International audienceThe 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