Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services

Abstract

Nowadays, more and more online content providers are offering multiple types of data services. To provide users with a better service experience, Quality of Experience (QoE) has been widely used in the delivery quality measurement of network services. How to accurately measure the QoE score for all types of network services has become a meaningful but difficult problem. To solve this problem, we proposed a unified QoE scoring framework that measures the user experience of almost all types of network services. The framework first uses a machine learning model (random forest) to classify network services, then selects different nonlinear expressions based on the type of service and comprehensively calculates the QoE score through the Quality of Service (QOS) metrics including transmission delay, packet loss rate, and throughput rate. Experiment results show that the proposed method has the ability to be applied on almost all the types of network traffic, and it achieves better QoE assessment accuracy than other works.