A generic quantitative relationship between Quality of Experience and Quality of Service

Document type: Journal Articles
Article type: Original article
Peer reviewed: Yes
Full text:
Author(s): Markus Fiedler, Tobias Hossfeld, Phuoc Tran-Gia
Title: A generic quantitative relationship between Quality of Experience and Quality of Service
Journal: IEEE Network
Year: 2010
Volume: 24
Issue: 2
Pagination: 36-41
ISSN: 0890-8044
Publisher: IEEE
URI/DOI: 10.1109/MNET.2010.5430142
ISI number: 000275661700006
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com
Authors e-mail: markus.fiedler@bth.se
Language: English
Abstract: Quality of Experience (QoE) ties together user perception, experience and expectations to application and network performance, typically expressed by Quality of Service (QoS) parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. On this background, this paper proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviours are
straighforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores (MOS) is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as
the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.
Subject: Telecommunications\General
Computer Science\Networks and Communications
Keywords: Full reference metric, no reference metric, reduced reference metric, MOS, PESQ, user rating, response time, cancellation rate, differential equation, IQX hypothesis
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