Prof. de Veciana honored by Intel and Cisco
Prof. Gustavo de Veciana was given the 'best technical talk award' by Cisco and Intel at their two day Video-Aware Wireless Networks (VAWN) Conference in San Jose, CA. The VAWN program was established in 2010 to help Intel and Cisco develop new ways for coping with the rapidly escalating demand for video consumption over wireless networks, and includes academics from USC, UCSD, Cornell, UT Austin, and Moscow State, as well as participation by Intel and Cisco engineers, and Verizon. The UT Austin team is leading an effort to re-engineer wireless networks by optimizing for perceptual quality. You can find out more about this research here. Prof. de Veciana's talk 'Rethinking Video Transport: Quality of Experience meets Multi-user Rate Adaptation was based on work with U.T. Ph.D. student Vinay Joseph. Their work looks at new ways at performing rate adaptation for video streaming so as to optimize users' eventual quality of experience. The novelty of this work lies in attempting to factor perceptual video quality as well as a user's behavioral responses to changes in quality. User perceived video quality depends on a variety of only partially understood factors, e.g., the application domain, content, compression, transport mechanism, and most importantly psycho-visual systems determining the ultimate Quality of Experience (QoE) of users. The talk centered on two key observations in addressing the problem of joint rate adaptation for video streams sharing a congested resource. First, is the observation that a user viewing a given video will experience temporal variations in the dependence of perceived video quality to the compression rate. Intuitively this is due to the possibly changing nature of the content, e.g., from a fast, action filled scene to a slower scene. Thus, in allocating rates to users sharing a congested resource, in particular a wireless system where additional temporal variability in users' capacity may be high, content dependent tradeoffs can be realized to deliver a better overall average perceived video quality. Second, is the observation that such adaptation of users' rates may result in temporal variations in video quality which combined with perceptual hysteresis effects will degrade users' QoE. The talk described development of an asymptotically optimal online algorithm, requiring minimal statistical information, for optimizing users' QoE by realizing tradeoffs across mean, variance and fairness. Simulations show that our approach achieves significant gains in viewers' QoE.