Intelligent Video QoE Prediction Model for Errorprone Networks

QoE Monitoring Platform for Video Delivery Networks

This paper presents a full video delivery network monitoring suite. Our monitoring tool offers a new view of a video delivery network, a view based on the quality perceived by final users. We measure, in real time and automatically, the perceived quality at the client side by means of the recently proposed PSQA technology. Moreover, we improve PSQA’s efficiency and robustness for video analysis...

CFA: A Practical Prediction System for Video QoE Optimization

Many prior efforts have suggested that Internet video Quality of Experience (QoE) could be dramatically improved by using data-driven prediction of video quality for different choices (e.g., CDN or bitrate) to make optimal decisions. However, building such a prediction system is challenging on two fronts. First, the relationships between video quality and observed session features can be quite ...

Video content-based QoE prediction for HEVC encoded videos delivered over IP networks

The recently released High Efficiency Video Coding (HEVC) standard, which halves the transmission bandwidth requirement of encoded video for almost the same quality when compared to H.264/AVC, and the availability of increased network bandwidth (e.g. from 2 Mbps for 3G networks to almost 100 Mbps for 4G/LTE) have led to the proliferation of video streaming services. Based on these major innovat...

QoE-driven Adaptation Scheme for Video Applications over Wireless Networks

User’s perceived Quality of Service or Quality of experience (QoE) is likely to be the major determining factor in the success of new multimedia applications over wireless/mobile networks. The primary aim of this paper is to present an adaptation scheme that is QoE-driven for optimizing content provisioning and network resource utilization for video applications over wireless networks. The prop...

LEVERAGING STANDARDS FOR VIDEO QoE