Quality is in the eye of the beholder: meeting users' requirements for Internet quality of service
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Intuitive Representation of Decision Trees Using General Rules and Exceptions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
User perception of adapting video quality
International Journal of Human-Computer Studies
Quantifying Skype user satisfaction
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Inside the bird's nest: measurements of large-scale live VoD from the 2008 olympics
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Understanding the impact of video quality on user engagement
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Inferring the QoE of HTTP video streaming from user-viewing activities
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YouTube everywhere: impact of device and infrastructure synergies on user experience
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
A longitudinal view of HTTP video streaming performance
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A case for a coordinated internet video control plane
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
Optimizing cost and performance for content multihoming
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
Server-based traffic shaping for stabilizing oscillating adaptive streaming players
Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
FCP: a flexible transport framework for accommodating diversity
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Developing a predictive model of quality of experience for internet video
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Downton abbey without the hiccups: buffer-based rate adaptation for HTTP video streaming
Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
An information-aware QoE-centric mobile video cache
Proceedings of the 19th annual international conference on Mobile computing & networking
Shedding light on the structure of internet video quality problems in the wild
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
Journal of Medical Systems
Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming With Festive
IEEE/ACM Transactions on Networking (TON)
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An imminent challenge that content providers, CDNs, third-party analytics and optimization services, and video player designers in the Internet video ecosystem face is the lack of a single "gold standard" to evaluate different competing solutions. Existing techniques that describe the quality of the encoded signal or controlled studies to measure opinion scores do not translate directly into user experience at scale. Recent work shows that measurable performance metrics such as buffering, startup time, bitrate, and number of bitrate switches impact user experience. However, converting these observations into a quantitative quality-of-experience metric turns out to be challenging since these metrics are interrelated in complex and sometimes counter-intuitive ways, and their relationship to user experience can be unpredictable. To further complicate things, many confounding factors are introduced by the nature of the content itself (e.g., user interest, genre). We believe that the issue of interdependency can be addressed by casting this as a machine learning problem to build a suitable predictive model from empirical observations. We also show that setting up the problem based on domain-specific and measurement-driven insights can minimize the impact of the various confounding factors to improve the prediction performance.