Energy Efficient Predictive Resource Allocation for VoD and Real-time Services

This paper studies how to exploit the predicted information to maximize energy efficiency (EE) of a system supporting hybrid services. To obtain an EE upper bound of predictive resource allocation, we jointly optimize resource allocation for video on-demand (VoD) and real-time (RT) services to maximize EE by exploiting perfect future large-scale channel gains. We find that the EE-optimal predictive resource allocation is a two-timescale policy, which makes a resources usage plan at the beginning of prediction window and allocates resources in each time slot. Analysis shows that if there is only VoD service, predicting large-scale channel gains and distribution of small-scale channel gains are necessary to achieve the EE upper bound. If there is only RT service, future large-scale channel gains cannot help improve EE. However, if there are both VoD and RT services, predicting large-scale channel gains of both kinds of users are helpful. A low-complexity is proposed, which is robust to prediction errors. Simulation results show that the optimal policy is superior to the relevant counterparts, and the heuristic policy can achieve higher EE than the optimal policy when the large-scale channel gains are inaccurate. Index Terms Energy efficiency, predictive resource allocation, VoD services, real-time services