Enhancing Quality of Experience (QoE) Assessment Models for Web Traffic.

Web applications are becoming the key services in today's networks (both fixed networks and mobile networks). Consideration of web service quality has become essential to provide the end users with satisfying Quality of Experience (QoE). In order to evaluate and manage the web quality, methods for QoE assessment are desired to estimate the service quality perceived by the end users. In this paper, we study a number of existing objective quality assessment models for assessing the QoE of web applications, and compare their performance with simulations to find out their individual advantages and limitations to use in practice. Simulation results show that the proposed QoE model can be applied for evaluating the quality of different web sources in the Long Term Evolution (LTE) networks, considering the lossy property of mobile networks. A fitting model is presented to describe the correlation between network Quality of Service and User QoE obtained in subjective lab test. To overcome the shortcomings of the existing models, this paper also proposes an enhanced QoE model which considers the effects of parameters such as page download size and content, browser cache setting as well as the packet losses and connection throughput in quality assessment e.g., page response time. To study other user related aspects in the evaluation of QoE, subjective tests in real systems and environments are planned as the next step.