15,442 research outputs found
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
Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming
Video streaming is in high demand by mobile users, as recent studies
indicate. In cellular networks, however, the unreliable wireless channel leads
to two major problems. Poor channel states degrade video quality and interrupt
the playback when a user cannot sufficiently fill its local playout buffer:
buffer underruns occur. In contrast to that, good channel conditions cause
common greedy buffering schemes to pile up very long buffers. Such
over-buffering wastes expensive wireless channel capacity.
To keep buffering in balance, we employ a novel approach. Assuming that we
can predict data rates, we plan the quality and download time of the video
segments ahead. This anticipatory scheduling avoids buffer underruns by
downloading a large number of segments before a channel outage occurs, without
wasting wireless capacity by excessive buffering. We formalize this approach as
an optimization problem and derive practical heuristics for segmented video
streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed
measurements show that our solution essentially eliminates playback
interruptions without significantly decreasing video quality
Streaming Video over HTTP with Consistent Quality
In conventional HTTP-based adaptive streaming (HAS), a video source is
encoded at multiple levels of constant bitrate representations, and a client
makes its representation selections according to the measured network
bandwidth. While greatly simplifying adaptation to the varying network
conditions, this strategy is not the best for optimizing the video quality
experienced by end users. Quality fluctuation can be reduced if the natural
variability of video content is taken into consideration. In this work, we
study the design of a client rate adaptation algorithm to yield consistent
video quality. We assume that clients have visibility into incoming video
within a finite horizon. We also take advantage of the client-side video
buffer, by using it as a breathing room for not only network bandwidth
variability, but also video bitrate variability. The challenge, however, lies
in how to balance these two variabilities to yield consistent video quality
without risking a buffer underrun. We propose an optimization solution that
uses an online algorithm to adapt the video bitrate step-by-step, while
applying dynamic programming at each step. We incorporate our solution into
PANDA -- a practical rate adaptation algorithm designed for HAS deployment at
scale.Comment: Refined version submitted to ACM Multimedia Systems Conference
(MMSys), 201
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