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Journal ArticleDOI

A “GAP-model” based framework for online VVoIP QoE measurement

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TLDR
A novel framework is presented that can provide online estimates of VVoIP QoE on network paths without end-user involvement and without requiring any video sequences and features the "G AP-model", which is an offline model ofQoE expressed as a function of measurable network factors such as bandwidth, delay, jitter, and loss.
Abstract
Increased access to broadband networks has led to a fast-growing demand for voice and video over IP (VVoIP) applications such as Internet telephony (VoIP), videoconferencing, and IP television (IPTV). For pro-active troubleshooting of VVoIP performance bottlenecks that manifest to end-users as performance impairments such as video frame freezing and voice dropouts, network operators cannot rely on actual end-users to report their subjective quality of experience (QoE). Hence, automated and objective techniques that provide real-time or online VVoIP QoE estimates are vital. Objective techniques developed to-date estimate VVoIP QoE by performing frame-to-frame peak-signal-to-noise ratio (PSNR) comparisons of the original video sequence and the reconstructed video sequence obtained from the sender-side and receiver-side, respectively. Since processing such video sequences is time consuming and computationally intensive, existing objective techniques cannot provide online VVoIP QoE. In this paper, we present a novel framework that can provide online estimates of VVoIP QoE on network paths without end-user involvement and without requiring any video sequences. The framework features the "G AP-model", which is an offline model of QoE expressed as a function of measurable network factors such as bandwidth, delay, jitter, and loss. Using the GAP-model, our online framework can produce VVoIP QoE estimates in terms of "Good", "Acceptable", or "Poor" (GAP) grades of perceptual quality solely from the on-line measured network conditions.

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Citations
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Journal ArticleDOI

From QoS to QoE: A Tutorial on Video Quality Assessment

TL;DR: A comprehensive survey of the evolution of video quality assessment methods, analyzing their characteristics, advantages, and drawbacks and identifying the future research directions of QoE is given.
Journal ArticleDOI

QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks

TL;DR: A new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks is illustrated.
Journal ArticleDOI

Quality of experience-driven adaptation scheme for video applications over wireless networks

TL;DR: The proposed scheme makes it possible for content providers to achieve optimum streaming suitable for the network and content type for a requested QoE, and is beneficial for network providers for network resource provision and planning, and therefore maximising existing network infrastructure by providing service differentiation.
Proceedings ArticleDOI

Content Clustering Based Video Quality Prediction Model for MPEG4 Video Streaming over Wireless Networks

TL;DR: This paper presents a two step approach to video quality prediction, where video sequences are classified into groups representing different content types using cluster analysis and video quality is predicted from network level parameter and application level parameters using Principal Component Analysis (PCA).
Journal ArticleDOI

Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks

TL;DR: The main aim of this paper is the prediction of video quality combining the application and network level parameters for all content types and confirmed that the video quality is more sensitive to network level compared to application level parameters.
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