scholarly journals Traffic Management of Video on Demand: An Analysis of Investments for Improving the End User's Quality of Experience

10.5772/63400 ◽  
2016 ◽  
Vol 8 ◽  
pp. 1 ◽  
Author(s):  
Francesca Di Pillo ◽  
Nathan Levialdi ◽  
Lupi Flavia
Keyword(s):  
Traffic Management ◽  
Quality Of Experience ◽  
Video On Demand ◽  
On Demand

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.

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 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.

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

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

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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