Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
Quantification of quality of experience for edge-based applications
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Application of the HoQ framework to improving QoE of broadband internet services
IEEE Network: The Magazine of Global Internetworking - Special issue on improving quality of experience for network services
A generic quantitative relationship between quality of experience and quality of service
IEEE Network: The Magazine of Global Internetworking - Special issue on improving quality of experience for network services
A study on a QoS/QoE correlation model for QoE evaluation on IPTV service
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
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The wide range of multimedia services has been attracting more users every day, while the increasing number of users plays an important role in the competitive advantage for network/service providers. Due to the fact that the users are the ones who pay for services, their satisfaction is an important goal for each provider in order to survive in the highly competitive market of multimedia services. Because of the nature of multimedia services, users can instantly detect any quality disturbances. For example, users might tolerate several interruptions during a download or upload process, but they may get irritated as soon as they experience a small amount of voice or video disturbance while they are watching their favorite movie through the internet for instance. Thus, quality of experience (QoE) should be maintained at a steady and acceptable level to satisfy users. To do so, it is necessary to identify the contributing factors in the network which affect multimedia quality. Generally speaking, delay, jitter, loss, and bandwidth have considerable impact on multimedia service quality. Because quality of service (QoS) is among the greatest impacting factors of QoE, it seems required to define a quantitative relation between QoE and QoS in order to keep the QoE at an acceptable level. This paper aims to benefit from psychophysics laws to devise quantitative relations which explain the interdependency of QoE and QoS. The proposed quantitative relations are expressed as equations which are then compared in theoretical and experimental setting to indicate which one can better reflect the relationship between QoE and QoS. A video streaming service affected by packet loss was also chosen as a candidate for our test bed. The test-bed results are then estimated by non linear regression and then validated by goodness of fit indexes. In this way, it can be examined how strongly each equation can express the dependency between QoE and QoS.