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From QoS distributions to QoE distributions: A system's perspective
University of Würzburg, DEU.
Norwegian University of Science and Technology, NOR.
Independent Researcher, FIN.
University of Zagreb, HRV.
Show others and affiliations
2020 (English)In: Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020 / [ed] De Turck F.,Chemouil P.,Wauters T.,Zhani M.F.,Cerroni W.,Pasquini R.,Zhu Z., Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 51-56, article id 9165426Conference paper, Published paper (Refereed)
Abstract [en]

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. © 2020 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 51-56, article id 9165426
Series
IEEE Conference on Network Softwarization (NetSoft), ISSN 2693-9789, E-ISSN 2693-9770
Keywords [en]
Quality of service, User experience, Analytical results, Mapping functions, Mean opinion scores, Numerical results, Qoe managements, Second order statistics, Service provider, System response time, Probability distributions
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:bth-20557DOI: 10.1109/NetSoft48620.2020.9165426ISI: 000623436400008Scopus ID: 2-s2.0-85091961030ISBN: 9781728156842 (print)OAI: oai:DiVA.org:bth-20557DiVA, id: diva2:1477139
Conference
6th IEEE Conference on Network Softwarization, NetSoft 2020, Virtual, Online, Belgium, 29 June 2020 through 3 July 2020
Note

open access

Available from: 2020-10-16 Created: 2020-10-16 Last updated: 2023-03-24Bibliographically approved

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Fiedler, Markus

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