nformation and communications technology (ICT) users find an ever growing set of IP-based applications and access networks from which to choose. With the availability of residential broadband, IP-based digital TV is also offered via Ethernet and asymmetric digital subscriber line (ADSL). Such a multitude of offers makes prices decrease, and competition between service and/or network providers increase. The customer finds itself in a strong position, being able to choose between different competing providers. Given similar pricing schemes, which are a primary decision aid for many users, their subsequent choices are then likely to be influenced by expected and experienced quality (i.e., through personal ratings of the perception and price-worthiness of a service). Consequently, the providers' interest in how users perceive usability, reliability, quality, and price-worthiness has increased. A provider needs to be able to observe and react quickly on quality problems, at best before the customer perceives them and considers churn. Facing this kind of quality competition, the concept of quality of experience (QoE) emerged, combining user perception, experience, and expectations with non-technical and technical parameters such as application-and network-level quality of service (QoS) [1] . We observe a non-uniform view of QoS by different stakeholders. While the International Telecommunication Union (ITU) standards focus on service quality toward the end user, the Internet Engineering Task Force's (IETF's) understanding of QoS relates to the capabilities of the network to provide packet transfer in a better-than-best-effort way. While the ITU view on QoS is user-centric [1], the IETF view on QoS is network-centric. This raises the question of how network-level QoS measurements and control relate to the user perception of a service. In order to choose appropriate measures to keep user-perceived service quality above an acceptance threshold, a provider needs to know how network-level QoS parameters translate into user-level QoE perception and vice versa. Obviously, QoE-QoS relationships depend on many aspects and parameters. A typical user-related measure is the mean opinion score (MOS), which can be determined from subjective ratings by real users or predicted from objective measurements of properties of the delivered goods such as audio, video, or files. From the area of usability engineering, reaction time thresholds for user perception are known as follows: 100 ms is roughly the boundary at which a user feels the system reacting instantaneously; less than 1 s keeps the user's thoughts, although a delay is perceived; less than 10 s keeps the user's attention, while exceeding 10 s implies the risk of the user abandoning the activity. The order of magnitude of the latter threshold has been confirmed by studies of user patience regarding web surfing [2] . Yet, no matter the networked application, users expect timely and complete data delivery. Ideally, a networked application should behave as if the enabling network was completely transparent. Streaming applications must maintain fluidity, conversations and interactive applications shall expose short response times, and downloads need to be able to efficiently stream large amounts of data in order to minimize waiting times [3] . Basic QoS problems on the network level relate to non-or late delivery, which might entail re-ordering. Thus,  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.
Fiedler et al. proposed a generic exponential function to depict the relationships among QoE and QoS parameters REF .
A generic quantitative relationship between quality of experience and quality of service
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Computer Science