QoE improvement using a human perceptual mechanism

Managing information uncertainties to enhance user's quality of experience

In the near future, we will be able to access to all sorts of information via information networks. Then, it is desired to realize a network control that can flexibly deal with various user requirements. Recently, quality of experience (QoE) for users is focused and various researches have studied on the improvement of it. Since QoE is a subjective value, it changes from moment to moment. There are various factors of QoE and they sometimes have uncertainty. Therefore, in order to improve the QoE of users, it is essential to perform a appropriate control under uncertain information. In this research, we use the bayesian attractor model that simulates the information perception in the human brain. We apply this model to a rate control of a video streaming application. We show that the proposed control method can realize QoE improvement under uncertain input information.

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