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1、A Quest for an Internet Video Quality-of-Experience MetricAthula Balachandran Vyas Sekar Aditya Akella Srinivasan Seshan Ion Stoica Hui Zhang Carnegie Mellon University University of Wisconsin Madison Stony Brook University University of California BerkeleyABSTRACTAn imminent challenge that content
2、providers, CDNs, thirdparty 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 studie
3、s 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 quali
4、ty-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 i
5、tself (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 i
6、nsights can minimize the impact of the various confounding factors to improve the prediction performance.Categories and Subject DescriptorsC.4 Performance of Systems: measurement techniques, performance attributesGeneral TermsHuman Factors, Measurement, Performance1. INTRODUCTIONWith the decreasing
7、cost of content delivery and the growing success of subscription and ad-based business models(e.g., 2), video traffic over the Internet is predicted to increase in the years to come, possibly even surpassing television based viewership in the future 3. An imminent challenge that all players in the I
8、nternet video ecosystemcontent providers, content delivery networks, analytics services, video player designers, and usersface is the lack of a standardized approach to measure the Quality-of-Experience (QoE) that different solutions provide. With the “coming of age” of this technology and the estab
9、lishment of industry standard groups (e.g., 13), such a measure will become a fundamental requirement to promote further innovation by allowing us to objectively compare different competing designs 11,17. The notion of QoE appears to many forms of media and has a rich history in the multimedia commu
10、nity (e.g., 9, 10,14, 15). However, Internet video introduces new effects interms of measuring both quality and experience: Measuring quality: Internet video is delivered using HTTP-based commodity technology over a largely unreliable network via existing CDN infrastructures. Consequently, the tradi
11、tional encoding-related measures of quality like Peak Signal- to-Noise Ratio are replaced by a suite of quality metrics that capture several effects introduced by the delivery mechanismbuffering, bitrate delivered, frame rendering rate, bitrate switching, and startup delay 6, 33. Measuring experienc
12、e: In the context of advertismentand subscription-supported services, the perceptual opinion of a user in a controlled study does not necessarily translate into objective measures of engagement that impact providers business objectives. Typical measures of engagement used today to approximate these
13、business objectives are in-the-wild measurements of user behavior; e.g., fraction of a particular video played and number of visits to the provider 6, 33. To obtain a robust QoE measure, we ideally need a unified and quantitative understanding of how low-level quality metrics impact measures of expe
14、rience. By unified, we want to see how the set of quality metrics taken together impact quality, as opposed to each metric in isolation. This is especially relevant since there are natural tradeoffs between the metrics; e.g., lower bitrate can ensure lower buffering but reduces the user experience.
15、Similarly, by quantitative, we want to go beyond a simple correlational understanding of “metric M impacts engagement”, to a stronger statement of the form “changing metric M from x to x changes engagement from y to y”.Unfortunately, the state of the art in our understanding of video QoE is limited
16、to a simple qualitative understanding of how individual metrics impact engagement 19. This leads to severe shortcomings for every component of the video ecosystem. For example, adaptive video players today resort to ad hoc tradeoffs between bitrate, startup delay, and buffering 16,20,32. Similarly, frameworks for multi-CDN optimization use primitive QoE metrics that only capture buffering effects without accounting for the impact
