  Predictive power allocation is conceived for energy-efficient video streaming
over mobile networks using deep reinforcement learning. The goal is to minimize
the accumulated energy consumption of each base station over a complete video
streaming session under the constraint that avoids video playback
interruptions. To handle the continuous state and action spaces, we resort to
deep deterministic policy gradient (DDPG) algorithm for solving the formulated
problem. In contrast to previous predictive power allocation policies that
first predict future information with historical data and then optimize the
power allocation based on the predicted information, the proposed policy
operates in an on-line and end-to-end manner. By judiciously designing the
action and state that only depend on slowly-varying average channel gains, we
reduce the signaling overhead between the edge server and the base stations,
and make it easier to learn a good policy. To further avoid playback
interruption throughout the learning process and improve the convergence speed,
we exploit the partially known model of the system dynamics by integrating the
concepts of safety layer, post-decision state, and virtual experiences into the
basic DDPG algorithm. Our simulation results show that the proposed policies
converge to the optimal policy that is derived based on perfect large-scale
channel prediction and outperform the first-predict-then-optimize policy in the
presence of prediction errors. By harnessing the partially known model, the
convergence speed can be dramatically improved.
