15,428 research outputs found
Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services
Title from PDF of title page, viewed on September 4, 2015Dissertation advisor: Deep MedhiVitaIncludes bibliographic references (pages 126-141)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015HTTP based online video streaming services have been consistently dominating
the online traffic for the past few years. Measuring and improving the performance of
these services is an important challenge. Traditional Quality-of-Service (QoS) metrics
such as packet loss, jitter and delay which were used for networked services are not easily
understood by the users. Instead, Quality-of-Experience (QoE) metrics which capture the
overall satisfaction are more suitable for measuring the quality as perceived by the users.
However, these QoE metrics have not yet been standardized and their measurement and
improvement poses unique challenges. In this work we first present a comprehensive
survey of the different set of QoE metrics and the measurement methodologies suitable
for HTTP based online video streaming services.
We then present our active QoE measurement tool Pytomo that measures the QoE
of YouTube videos. A case study on the measurement of QoE of YouTube videos when
accessed by residential users from three different Internet Service Providers (ISP) in a
metropolitan area is discussed. This is the first work that has collected QoE data from
actual residential users using active measurements for YouTube videos. Based on these
measurements we were able to study and compare the QoE of YouTube videos across
multiple ISPs. We also were able to correlate the QoE observed with the server clusters
used for the different users. Based on this correlation we were able to identify the server
clusters that were experiencing diminished QoE.
DynamicAdaptive Streaming overHTTP (DASH) is an HTTP based video streaming
that enables the video players to adapt the video quality based on the network conditions.
We next present a rate adaptation algorithm that improves the QoE of DASH
video streaming services that selects the most optimum video quality. With DASH the
video server hosts multiple representation of the same video and each representation is
divided into small segments of constant playback duration. The DASH player downloads
the appropriate representation based on the network conditions, thus, adapting the video
quality to match the conditions. Currently deployed Adaptive Bitrate (ABR) algorithms
use throughput and buffer occupancy to predict segment fetch times. These algorithms
assume that the segments are of equal size. However, due to the encoding schemes employed
this assumption does not hold. In order to overcome these limitations, we propose
a novel Segment Aware Rate Adaptation algorithm (SARA) that leverages the knowledge
of the segment size variations to improve the prediction of segment fetch times. Using
an emulated player in a geographically distributed virtual network setup, we compare the
performance of SARA with existing ABR algorithms. We demonstrate that SARA helps
to improve the QoE of the DASH video streaming with improved convergence time, better
bitrate switching performance and better video quality. We also show that unlike the existing
adaptation schemes, SARA provides a consistent QoE irrespective of the segment
size distributions.Introduction -- Measurement of QoE for Online Video Streaming Services: A Literature Survey -- Pytomo: A Tool for measuring QoE of YouTube Videos -- Case Study: QoE across three Internet Service Providers in a Metropolitan Area -- Adaptive Bitrate Algorithms for DASH -- Segment Aware Rate Adaptation for DASH -- Performance Evaluation of SARA -- Conclusion and Future Research --Appendix A. Sample MPD Fil
Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions
Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined
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
QoE-centric management of advanced multimedia services
Over the last years, multimedia content has become more prominent than ever. Particularly, video streaming is responsible for more than a half of the total global bandwidth consumption on the Internet. As the original Internet was not designed to deliver such real-time, bandwidth-consuming applications, a serious challenge is posed on how to efficiently provide the best service to the users. This requires a shift in the classical approach used to deliver multimedia content, from a pure Quality of Service (QoS) to a full Quality of Experience (QoE) perspective. While QoS parameters are mainly related to low-level network aspects, the QoE reflects how the end-users perceive a particular multimedia service. As the relationship between QoS parameters and QoE is far from linear, a classical QoS-centric delivery is not able to fully optimize the quality as perceived by the users. This paper provides an overview of the main challenges this PhD aims to tackle in the field of end-to-end QoE optimization of video streaming services and, more precisely, of HTTP Adaptive Streaming (HAS) solutions, which are quickly becoming the de facto standard for video delivery over the Internet
Objective assessment of region of interest-aware adaptive multimedia streaming quality
Adaptive multimedia streaming relies on controlled
adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication
link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are
perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality
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