Deriving QoE in systems: from fundamental relationships to a QoE-based Service-level Quality Index

Please always quote using this URN: urn:nbn:de:bvb:20-opus-235597
  • With Quality of Experience (QoE) research having made significant advances over the years, service and network providers aim at user-centric evaluation of the services provided in their system. The question arises how to derive QoE in systems. In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to varying distributions of user rating scores for different test conditions. Such models are commonly exploited by providers to derive various QoE metrics in their system,With Quality of Experience (QoE) research having made significant advances over the years, service and network providers aim at user-centric evaluation of the services provided in their system. The question arises how to derive QoE in systems. In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to varying distributions of user rating scores for different test conditions. Such models are commonly exploited by providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question then becomes how to combine (a) user rating distributions obtained from subjective studies, and (b) system parameter distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove fundamental relationships for the derivation of QoE in systems, thus providing an important link between the QoE community and the systems community. In our numerical examples, we focus mainly on QoE metrics. We furthermore provide a more generalized view on quantifying the quality of systems by defining a QoE-based Service-level Quality Index. This index exploits the fact that quality can be seen as a proxy measure for utility. Following the assumption that not all user sessions should be weighted equally, we aim to provide a generic framework that can be utilized to quantify the overall utility of a service delivered by a system.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Tobias Hoßfeld, Poul E. Heegaard, Lea Skrorin-Kapov, Martín Varela
URN:urn:nbn:de:bvb:20-opus-235597
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Language:English
Parent Title (English):Quality and User Experience
ISSN:2366-0139
Year of Completion:2020
Volume:5
Article Number:7
Source:Quality and User Experience 5, 7 (2020). https://doi.org/10.1007/s41233-020-00035-0
DOI:https://doi.org/10.1007/s41233-020-00035-0
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Tag:Expected MOS; Expected QoE; Good-or-Better (GoB); QoE fundamentals; QoS-QoE mapping functions; Service-level Quality Index (SQI)
Release Date:2021/06/24
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International