From QoS distributions to QoE distributions: A system's perspective

Tobias Hosfeld, Poul E. Heegaard, Martin Varela, Lea Skorin-Kapov, Markus Fiedler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating 'good or better', etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Conference on Network Softwarization
Subtitle of host publicationBridging the Gap Between AI and Network Softwarization, NetSoft 2020
EditorsFilip De Turck, Prosper Chemouil, Tim Wauters, Mohamed Faten Zhani, Walter Cerroni, Rafael Pasquini, Zuqing Zhu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-56
Number of pages6
ISBN (Electronic)9781728156842
DOIs
StatePublished - Jun 2020
Externally publishedYes
Event6th IEEE Conference on Network Softwarization, NetSoft 2020 - Virtual, Online, Belgium
Duration: 29 Jun 20203 Jul 2020

Publication series

NameProceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020

Conference

Conference6th IEEE Conference on Network Softwarization, NetSoft 2020
Country/TerritoryBelgium
CityVirtual, Online
Period29/06/203/07/20

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