  In this paper, we propose a framework to maximize energy efficiency (EE) of a
system supporting real-time (RT) and non-real-time services by exploiting
future average channel gains of mobile users, which change in the timescale of
seconds and are reported predictable within a minute-long time window. To
demonstrate the potential of improving EE by jointly optimizing resource
allocation for both services by harnessing both future average channel gains
and current instantaneous channel gains, we optimize a two-timescale policy
with perfect prediction, by taking orthogonal frequency division multiple
access system serving RT and video-on-demand (VoD) users as an example.
Considering that fine-grained prediction for every user is with high cost, we
propose a heuristic policy that only needs to predict the median of average
channel gains of VoD users. Simulation results show that the optimal policy
outperforms relevant counterparts, indicating the necessity of the joint
optimization for both services and for two timescales. Besides, the heuristic
policy performs closely to the optimal policy with perfect prediction while
becomes superior with large prediction errors. This suggests that the EE gain
over non-predictive policies can be captured with coarse-grained prediction.
