Monday, April 18, 2016 - 12:00pm to 1:00pm
Location:Traffic21 Classroom 6501 Gates & Hillman Centers
Speaker:JUNCHEN JIANG, Ph.D. Student http://www.cs.cmu.edu/~junchenj/
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Many prior efforts have suggested that Internet video Quality of Experience (QoE) could be dramatically improved by using data-driven prediction of video quality for different choices (e.g., CDN or bitrate) to make optimal decisions. However, building such a prediction system is challenging on two fronts. First, the relationships between video quality and observed session features can be quite complex. Second, video quality changes dynamically. Thus, we need a prediction model that is (a) expressive enough to capture these complex relationships and (b) capable of updating quality predictions in near real-time. Unfortunately, several seemingly natural solutions (e.g., simple machine learning approaches and simple network models) fail on one or more fronts. Thus, the potential benefits promised by these prior efforts remain unrealized. We address these challenges and present the design and implementation of Critical Feature Analytics (CFA). The design of CFA is driven by domain-specific insights that video quality is typically determined by a small subset of critical features whose criticality persists over several tens of minutes. This enables a scalable and accurate workflow where we automatically learn critical features for different sessions on coarse-grained timescales, while updating quality predictions in near real-time. Using a combination of a real-world pilot deployment and trace-driven analysis, we demonstrate that CFA leads to significant improvements in video quality; e.g., 32% less buffering time and 12% higher bitrate than a random decision maker. This is a joint work with Vyas Sekar, Hui Zhang, and Ion Stoica Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.