Energy-efficient predictive video streaming under demand uncertainties

Highly predictable users' location and traffic have enabled a new video delivery paradigm over wireless networks referred to as Predictive Resource Allocation (PRA). Existing research assumes perfect prediction of information in order to derive the performance bounds of PRA and define its gains over conventional Resource Allocation (RA). In this paper we sustain the application of energy-efficient PRA under prediction uncertainties. To that end, we propose a stochastic robust PRA scheme that models the uncertainty in future demands and incorporates them in the mathematical formulation. A linear Recourse Programming (RP) model is adopted in order to represent the trade-off between the energy-savings and the risk of wasting resources while considering the probability of a user terminating or skipping the video session. Thus, avoids prebuffering the video chunks that might be skipped by the user. A low complexity near optimal algorithm is then introduced to provide real-time solutions for the formulated RP model. Simulation results demonstrate the ability of the introduced robust PRA to deliver energy-efficient video streaming with lower resources than the existing PRA while promising QoS satisfaction. These results provide the impetus to implement the robust PRA in future wireless networks.