Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

PredNet and Predictive Coding: A Critical Review

PredNet, a deep predictive coding network developed by Lotter et al., combines a biologically inspired architecture based on the propagation of prediction error with self-supervised representation learning in video. While the architecture has drawn a lot of attention and various extensions of the model exist, there is a lack of a critical analysis. We fill in the gap by evaluating PredNet both as an implementation of the predictive coding theory and as a self-supervised video prediction model using a challenging video action classification dataset. We design an extended model to test if conditioning future frame predictions on the action class of the video improves the model performance. We show that PredNet does not yet completely follow the principles of predictive coding. The proposed top-down conditioning leads to a performance gain on synthetic data, but does not scale up to the more complex real-world action classification dataset. Our analysis is aimed at guiding future research on similar architectures based on the predictive coding theory

No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives

Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments