Professor Alan Bovik and his research team were recognized for algorithms that optimize streaming media at the 72nd Annual Technology & Engineering Emmy® Awards. While winners were announced earlier this year, the awards were presented in a virtual ceremony livestreamed on November 4.
Given the tremendous increases in video traffic, which account for the majority of mobile data traffic, there has been a dramatic shift towards over-the-top video streaming. Due to limits on wireless network capacity, and with an increasingly knowledgeable base of consumer users demanding higher quality video display services, accounting for an end user's quality of experience (QoE) has become an essential measure of network performance. QoE refers to a viewer's holistic perception and satisfaction with a given communication network service.
An adaptive streaming system must always mediate trade-offs among a number of performance metrics, including average bitrates, bitrate variation, the frequencies and durations of re-buffering events, and startup delay. Many video streaming providers, like Netflix, are oriented towards avoiding re-buffering (or stall) events at all costs. Given the increasing demand for video streaming in mobile devices, low bandwidth conditions and small screen viewing conditions are intertwined with the standard tradeoffs between bitrate changes and re-buffering events.
Netflix and other video content providers are tasked with delivering top-notch video quality to hundreds of millions of subscribers. As these providers continue to increase the sizes of their collection, a substantial percentage of the acquired video content will contain visual artifacts produced at the time of the video's production. These artifacts can include de-interlacing errors, up-sampling distortions, and other annoying visual defects that could greatly reduce the perceptual quality and ultimately the quality of experience of the subscriber/viewer.
WNCG Profs. François Baccelli and Gustavo de Veciana and alumnus Pranav Madadi proposed a proposed a stochastic geometry framework to study temporal performance variations experienced by a mobile user in a cellular network. The focus is on the variations of the Signal-to-Noise-Ratio (SNR) and the downlink Shannon rate experienced when the user moves across a Poisson cellular network on the Euclidean plane.
