7,112 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 userâs 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 usersâ 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 userâs needs and desires when using the service, emphasizing the userâs 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
Q-AIMD: A Congestion Aware Video Quality Control Mechanism
Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called Q-AIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, Q-AIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them
Control of Multiple Remote Servers for Quality-Fair Delivery of Multimedia Contents
This paper proposes a control scheme for the quality-fair delivery of several
encoded video streams to mobile users sharing a common wireless resource. Video
quality fairness, as well as similar delivery delays are targeted among
streams. The proposed controller is implemented within some aggregator located
near the bottleneck of the network. The transmission rate among streams is
adapted based on the quality of the already encoded and buffered packets in the
aggregator. Encoding rate targets are evaluated by the aggregator and fed back
to each remote video server (fully centralized solution), or directly evaluated
by each server in a distributed way (partially distributed solution). Each
encoding rate target is adjusted for each stream independently based on the
corresponding buffer level or buffering delay in the aggregator. Communication
delays between the servers and the aggregator are taken into account. The
transmission and encoding rate control problems are studied with a
control-theoretic perspective. The system is described with a multi-input
multi-output model. Proportional Integral (PI) controllers are used to adjust
the video quality and control the aggregator buffer levels. The system
equilibrium and stability properties are studied. This provides guidelines for
choosing the parameters of the PI controllers. Experimental results show the
convergence of the proposed control system and demonstrate the improvement in
video quality fairness compared to a classical transmission rate fair streaming
solution and to a utility max-min fair approach
Video Stream Adaptation In Computer Vision Systems
Computer Vision (CV) has been deployed recently in a wide range of applications, including surveillance and automotive industries. According to a recent report, the market for CV technologies will grow to $33.3 billion by 2019. Surveillance and automotive industries share over 20% of this market. This dissertation considers the design of real-time CV systems with live video streaming, especially those over wireless and mobile networks. Such systems include video cameras/sensors and monitoring stations. The cameras should adapt their captured videos based on the events and/or available resources and time requirement. The monitoring station receives video streams from all cameras and run CV algorithms for decisions, warnings, control, and/or other actions. Real-time CV systems have constraints in power, computational, and communicational resources. Most video adaptation techniques considered the video distortion as the primary metric. In CV systems, however, the main objective is enhancing the event/object detection/recognition/tracking accuracy. The accuracy can essentially be thought of as the quality perceived by machines, as opposed to the human perceptual quality. High-Efficiency Video Coding (HEVC) is a recent encoding standard that seeks to address the limited communication bandwidth problem as a result of the popularity of High Definition (HD) videos. Unfortunately, HEVC adopts algorithms that greatly slow down the encoding process, and thus results in complications in real-time systems.
This dissertation presents a method for adapting live video streams to limited and varying network bandwidth and energy resources. It analyzes and compares the rate-accuracy and rate-energy characteristics of various video streams adaptation techniques in CV systems. We model the video capturing, encoding, and transmission aspects and then provide an overall model of the power consumed by the video cameras and/or sensors. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. We validate and analyze the power consumption models of each phase as well as the aggregate power consumption model through extensive experiments. The analysis includes examining individual parameters separately and examining the impacts of changing more than one parameter at a time. For HEVC, we develop an algorithm that predicts the size of the block without iterating through the exhaustive Rate Distortion Optimization (RDO) method. We demonstrate the effectiveness of the proposed algorithm in comparison with existing algorithms. The proposed algorithm achieves approximately 5 times the encoding speed of the RDO algorithm and 1.42 times the encoding speed of the fastest analyzed algorithm
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