Version 1
: Received: 9 December 2019 / Approved: 11 December 2019 / Online: 11 December 2019 (04:46:57 CET)
How to cite:
Wahab, A.; Schormans, J.; Ahmad, N. Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications. Preprints2019, 2019120148. https://doi.org/10.20944/preprints201912.0148.v1
Wahab, A.; Schormans, J.; Ahmad, N. Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications. Preprints 2019, 2019120148. https://doi.org/10.20944/preprints201912.0148.v1
Wahab, A.; Schormans, J.; Ahmad, N. Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications. Preprints2019, 2019120148. https://doi.org/10.20944/preprints201912.0148.v1
APA Style
Wahab, A., Schormans, J., & Ahmad, N. (2019). Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications. Preprints. https://doi.org/10.20944/preprints201912.0148.v1
Chicago/Turabian Style
Wahab, A., John Schormans and Nafi Ahmad. 2019 "Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications" Preprints. https://doi.org/10.20944/preprints201912.0148.v1
Abstract
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.
Keywords
Quality of Experience; Quality of Service; QoE evaluation video on demand; Quality of Service; QoS correlation; subjective testing
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
