Video Quality Appraisal Based on Ml Modelling in Wireless Networks

Abstract

Today, user experience is considered as a valid indicator for wireless service providers to indicate the overall end to end Network operation. In order to compete for prominent market share, different wireless network operators and service providers should retain and improve customer subscription and satisfy customer requirements. To fulfill these needs, they require an efficient Quality of Experience (QoE) estimation. QoE deals with user perception and can vary due to user expectation and context. However, QoE evaluation is very expensive and time consuming process since it requires human participation. Therefore there is a need for a tool that can objectively measure the QoE with reasonable accuracy in real-time. Cisco study shows that by 2020 video content will grow by nearly 75 to 80 percent of total Internet traffic. As wireless networks over which videos are transmitted are very un-predictive, it is apparent to use machine learning based approach to measure QoE for high performance and value addition by service providers.