  The emerging video applications greatly increase the demand in network
bandwidth that is not easy to scale. To provide higher quality of experience
(QoE) under limited bandwidth, a recent trend is to leverage the heterogeneity
of quality preferences across individual users. Although these efforts have
suggested the great potential benefits, service providers still have not
deployed them to realize the promised QoE improvement. The missing piece is an
automation of online per-user QoE modeling and optimization scheme for new
users. Previous efforts either optimize QoE by known per-user QoE models or
learn a user's QoE model by offline approaches, such as analysis of video
viewing history and in-lab user study. Relying on such offline modeling is
problematic, because QoE optimization will start late for collecting enough
data to train an unbiased QoE model. In this paper, we propose VidHoc, the
first automatic system that jointly personalizes QoE model and optimizes QoE in
an online manner for each new user. VidHoc can build per-user QoE models within
a small number of video sessions as well as maintain good QoE. We evaluate
VidHoc in a pilot deployment to fifteen users for four months with the care of
statistical validity. Compared with other baselines, the results show that
VidHoc can save 17.3% bandwidth while maintaining the same QoE or improve QoE
by 13.9% with the same bandwidth.

Networking and Internet Architecture