Utilization of Stochastic Modeling for Green Predictive Video Delivery Under Network Uncertainties
Predictive resource allocation (PRA) has gained momentum in the network research community as a way to cope with the exponential increase in video traffic. Existing PRA schemes have demonstrated profound energy savings and ubiquitous quality of service (QoS) satisfaction under idealistic prediction of future network states. In this paper, we relax the main assumption of existing PRA work and tackle uncertainties in predicted information which resulted from space and time variation of the network load and users demands. A robust green PRA (R-GPRA) is proposed to: model the uncertainties as random variables, ensure a probabilistic satisfaction of QoS constraints, and follow a risk-aware preallocation of future demand. A recourse programming model is used to represent the tradeoff between the energy-savings and the risk of wasting resources while considering the probability of a user terminating the video session at each time slot. Thus, the scheme prevents the network from prebuffering the future video content that might be skipped by the user. Similarly, a chance constrained programming model is proposed to provide a probabilistic QoS representation to guarantee that the sum of resources, predetermined to video streaming users, do not surpass the total time-varying network capacity.
We prove that a near-optimal solution is attainable by proposing a guided heuristic search with small optimality gap to numerical methods. Simulation results demonstrate the ability of R-GPRA to deliver energy-efficient video streaming with less resources than existing PRA while promising QoS satisfaction. These results provide the incentive to implement the R-GPRA in future wireless networks. (Read More)