Experience prediction is one key component in today’s multimedia delivery. Knowing user’s viewing experience allows online video service providers (e.g., Netflix, YouTube) to create value for their customers by providing personalized content and service. However, individual experience prediction is a challenging problem since viewing experience (defined as Quality of Experience in this thesis) is a multifaceted quantity and it is rather personal and subjective. The existing methods for quantifying Quality of Experience (QoE) target at estimating how the video quality is perceived by users, neglecting the hedonic part of experience (the degree of enjoyment of a user watching a video). Quite naturally, these methods consider only factors related to video perceptual quality (purely from video), which is insufficient to properly assess viewing experience. The research reported in this thesis attempts for the first time at shifting the paradigm for perceptual quality modeling, towards measuring and predicting the level of enjoyable viewing experience a user has with a video. In particular, it focuses on exploiting the potential value of user factors (information from users) and investigate their influences on QoE prediction. The goal of this thesis is to develop a feasible method for predicting the individual viewing experiences in terms of perceptual quality and enjoyment by taking multiple influencing factors into account. Here, the influencing factors are taken from both video (e.g., related to perceptual quality) and user (user factors, e.g., interest. personality). We take three major steps to accomplish this goal. We first deploy a subjective experiment to understand the relationship between perceptual quality and enjoyment, and how their influencing factors form the final viewing experience. With a set of identified influencing factors, we then propose a new QoE prediction model which processes both user and video information to predict individual experience (i.e., either perceptual quality or enjoyment). We show that combining information from video and user enables better prediction performance as compared to only considering information from video related to perceptual quality. Our third step tackles the problem of reliable data collection for the individual QoE research. We developed an open-sourced Facebook application, named YouQ, as an experimental platform for automatic user information collection from social media while performing an online QoE subjective experiment. We show that YouQ can produce reliable results as compared to a controlled laboratory experiment, both in terms of QoE and of quantification of user factors and traits. As a result, a complete, feasible method for individual QoE prediction is presented in this thesis. Based on the findings presented in this thesis, we reflect on the contribution and make recommendations for future research directions, which we think are substantial and promising for individual QoE prediction.@en

Most automatic Quality of Experience (QoE) assessment models have so far aimed at predicting the QoE of a video as experienced by an average user, and solely based on perceptual characteristics of the video being viewed. The importance of other characteristics, such as those related to the video content being watched, or those related to an individual user have been largely neglected. This is suboptimal in view of the fact that video viewing experience is individual and multifaceted, considering the perceived quality (related to coding or network-induced artifacts), but also other -- more hedonic - aspects, like enjoyment. In this paper, we propose an expanded model which aims to assess QoE of a given video, not only in terms of perceived quality but also of enjoyment, as experienced by a specific user. To do so, we feed the model not only with information extracted from the video (related to both perceived quality and content), but also with individual user characteristics, such as interest, personality and gender. We assess our expanded QoE model based on two publicly available QoE datasets, namely i_QoE and CP-QAE-I. The results show that combining various types of characteristics enables better QoE prediction performance as compared to only considering perceptual characteristics of the video, both when targeting perceived quality and enjoyment.@en