A generic quantitative relationship between quality of experience and quality of service

TLDR

Quality of experience links user perception and expectations to application and network performance, typically expressed by QoS parameters, and quantitative relationships between QoE and QoS are needed to build effective QoE control mechanisms. This article proposes the IQX hypothesis, a generic exponential formula linking QoE and QoS parameters. The IQX formula relates changes in QoE to QoS changes relative to the current QoE level, is simple to fit, and has interpretable limit behaviors. The IQX hypothesis was validated for streaming services, where QoE (Mean Opinion Scores) depends on loss and reordering ratio caused by jitter, and for web surfing it outperformed previously published logarithmic functions, making it a strong candidate for deriving QoE–QoS relationships.

Abstract

Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article 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 behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores 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.