  The unprecedented growth of mobile video traffic is adding significant
pressure to the energy drain at both the network and the end user. Energy
efficient video transmission techniques are thus imperative to cope with the
challenge of satisfying user demand at sustainable costs. In this paper, we
investigate how predicted user rates can be exploited for energy efficient
video streaming with the popular HTTP-based Adaptive Streaming (AS) protocols
(e.g. DASH). To this end, we develop an energy-efficient Predictive Green
Streaming (PGS) optimization framework that leverages predictions of wireless
data rates to achieve the following objectives 1) minimize the required
transmission airtime without causing streaming interruptions, 2) minimize total
downlink Base Station (BS) power consumption for cases where BSs can be
switched off in deep sleep, and 3) enable a trade-off between AS quality and
energy consumption. Our framework is first formulated as a Mixed Integer Linear
Program (MILP) where decisions on multi-user rate allocation, video segment
quality, and BS transmit power are jointly optimized. Then, to provide an
online solution, we present a polynomial-time heuristic algorithm that
decouples the PGS problem into multiple stages. We provide a performance
analysis of the proposed methods by simulations, and numerical results
demonstrate that the PGS framework yields significant energy savings.
