scholarly journals Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

2020 ◽  
Vol 10 (10) ◽  
pp. 3662 ◽  
Author(s):  
Abdul Wahab ◽  
Nafi Ahmad ◽  
John Schormans
Keyword(s):  
Error Propagation ◽  
Quality Of Experience ◽  
Statistical Error ◽  
Video On Demand ◽  
Sampling Errors ◽  
On Demand ◽  
Subjective Testing ◽  
Qos Parameters ◽  
Latency Jitter

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.

scholarly journals Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

2019 ◽  
Author(s):  
Abdul Wahab ◽  
John Schormans ◽  
Nafi Ahmad
Keyword(s):  
Error Propagation ◽  
Quality Of Experience ◽  
Statistical Error ◽  
Video On Demand ◽  
Sampling Errors ◽  
Mathematical Functions ◽  
On Demand ◽  
Subjective Testing ◽  
Qos Parameters

In addition to the traditional QoS metrics of delay, delay jitter, and packet loss probability (PLP), Quality of Experience (QoE) is now widely accepted as a numerical proxy for actual user experience. The literature has reported many mathematical mappings between QoE and QoS. These QoS parameters are measured by the network providers using sampling. There are some papers studying sampling errors in QoS measurements; however there is no account of propagation of these sampling errors to QoE evaluation. In this paper, we used industrially acquired measurements of PLP and jitter to evaluate the sampling errors and correlation in measurements. Focussing on Video-on-demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLP and jitter. The resulting mathematical functions of QoE and theory of error propagation was used to evaluate the propagated error in QoE, and this error was represented as confidence interval. Using the guidelines of UK government for sampling, our results indicate that confidence intervals around estimated QoE in a busy hour can be between MOS=1 to MOS=5 at targeted operating point of QoS parameters. These results are a new perspective on QoE evaluation, and are of great significance to all organisations that need to estimate the QoE VoD applications precisely.

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

2016 ◽  
Vol 18 (1) ◽  
pp. 401-418 ◽  
Author(s):  
Parikshit Juluri ◽  
Venkatesh Tamarapalli ◽  
Deep Medhi
Keyword(s):  
Quality Of Experience ◽  
Video On Demand ◽  
On Demand

scholarly journals Assessing Quality of Experience of IPTV and Video on Demand Services in Real-Life Environments

2010 ◽  
Vol 56 (4) ◽  
pp. 458-466 ◽  
Author(s):  
Nicolas Staelens ◽  
Stefaan Moens ◽  
Wendy Van den Broeck ◽  
Ilse Marien ◽  
Brecht Vermeulen ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Real Life ◽  
Video On Demand ◽  
On Demand

Quality of Experience in Mobile Peer-to-Peer Streaming Systems

2012 ◽  
pp. 196-241
Author(s):  
Florence Agboma
Keyword(s):  
Quality Of Experience ◽  
Business Models ◽  
Prediction Models ◽  
Content Delivery ◽  
Peer To Peer ◽  
Network Resource ◽  
Relative Contribution ◽  
Qos Parameters ◽  
Streaming Systems

This chapter considers the various parameters that affect the user’s Quality-of-Experience (QoE) in mobile peer-to-peer streaming systems, which are a form of content delivery network. Network and content providers do not necessarily focus on users’ QoE when designing the content delivery strategies and business models. The outcome of this is quite often the over-provisioning of network resources and also a lack of knowledge in respect to the user’s satisfaction. The focus is the methodology for quantifying the user’s perception of service quality for mobile video services and user contexts. The statistical technique of discriminant analysis is employed in defining prediction models to map Quality-of-Service (QoS) parameters onto estimates of the user’s QoE ratings. The chapter considers the relative contribution of the QoS parameters to predicting user responses. The chapter also demonstrates the value of the prediction models in developing QoE management strategies in order to optimize network resource utilization. To investigate the versatility of the framework, a feasibility study was applied to a P2P TV system. P2P systems continue to develop and as such, not a lot is known about their QoE characteristics, which situation this chapter seeks to remedy.

Quality of Experience vs. QoS in Video Transmission

2010 ◽  
pp. 352-376 ◽  
Author(s):  
André F. Marquet ◽  
Jânio M. Monteiro ◽  
Nuno J. Martins ◽  
Mario S. Nunes
Keyword(s):  
Communication Networks ◽  
Video Transmission ◽  
Quality Of Experience ◽  
Video Quality ◽  
Subjective Assessment ◽  
Ip Networks ◽  
Point Of View ◽  
Qos Parameters ◽  
Quality Video

In legacy television services, user centric metrics have been used for more than twenty years to evaluate video quality. These subjective assessment metrics are usually obtained using a panel of human evaluators in standard defined methods to measure the impairments caused by a diversity of factors of the Human Visual System (HVS), constituting what is also called Quality of Experience (QoE) metrics. As video services move to IP networks, the supporting distribution platforms and the type of receiving terminals is getting more heterogeneous, when compared with classical video distributions. The flexibility introduced by these new architectures is, at the same time, enabling an increment of the transmitted video quality to higher definitions and is supporting the transmission of video to lower capability terminals, like mobile terminals. In IP Networks, while Quality of Service (QoS) metrics have been consistently used for evaluating the quality of a transmission and provide an objective way to measure the reliability of communication networks for various purposes, QoE metrics are emerging as a solution to address the limitations of conventional QoS measuring when evaluating quality from the service and user point of view. In terms of media, compressed video usually constitutes a very interdependent structure degrading in a non-graceful manner when exposed to Binary Erasure Channels (BEC), like the Internet or wireless networks. Accordingly, not only the type of encoder and its major encoding parameters (e.g. transmission rate, image definition or frame rate) contribute to the quality of a received video, but also QoS parameters are usually a cause for different types of decoding artifacts. As a result of this, several worldwide standard entities have been evaluating new metrics for the subjective assessment of video transmission over IP networks. In this chapter we are especially interested in explaining some of the best practices available to monitor, evaluate and assure good levels of QoE in packet oriented networks for rich media applications like high quality video streaming. For such applications, service requirements are relatively loose or difficult to quantify and therefore specific techniques have to be clearly understood and evaluated. By the mid of the chapter the reader should have understood why even networks with excellent QoS parameters might have QoE issues, as QoE is a systemic approach that does not relate solely to QoS but to the ensemble of components composing the communication system.

A practical methodology for guaranteeing quality of service for video-on-demand

2000 ◽  
Vol 10 (1) ◽  
pp. 166-178 ◽  
Author(s):  
J. Zamora ◽  
S. Jacobs ◽  
A. Eleftheriadis ◽  
Shih-Fu Chang ◽  
D. Anastassiou
Keyword(s):  
Quality Of Service ◽  
Video On Demand ◽  
On Demand
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