Evaluating QoE in VoIP networks with QoS mapping and machine learning algorithms

Abstract

The quality of experience (QoE) of the end-users is a critical criterion of measurement in VoIP (Voice over Internet Protocol) systems for technical and commercial purposes. We investigate how quality of service (QoS) influences QoE and assesses the QoE in VoIP communication. Our contributions are three-fold. First, the impacts of QoS on QoE are comprehensively analyzed by experimental means and an association test method, instead of independently studying each parameter. Second, an algorithm is proposed to integrate the effects of QoS parameters with spatial or temporal characteristics on QoE. Third, we apply machine learning regression algorithms with QoS impairments, noise and echo impairments to nonintrusive voice quality prediction in different network environments. The results from numerous experiments show that fairly accurate prediction can be obtained from these models. Our work will achieve a more accurate evaluation of the QoE in VoIP by using QoS parameters, clarify the influence of IP network environments, noise and echo impairments on the quality and reliability of VoIP traffic, and provide QoS parameter requirements for the VoIP application that runs at the desired QoE level.