Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks

Abstract

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 energyefficient 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.Index Terms-Wireless access networks, energy efficiency, resource allocation, dynamic adaptive streaming over HTTP (DASH), mobility, channel state prediction.H. Abou-zeid is with the

Toward green media delivery: location-aware opportunities and approaches

“…Furthermore, a user on a highway traversing two cells, can be pre-allocated the requested video content in the first cell, while the second cell is switched off without causing any video stalling. More details on the potential and architecture of such a predictive access strategies are presented in [4], while [23,24] discuss efficient predictive algorithms that minimize video degradations and ensure quality fairness among users.…”

“…Furthermore, it is possible to make BS On/Off switching plans that turn the BS completely off at the times within T where no users require resources. With this approach, bulk user allocations can also be grouped together to prebuffer content and satisfy all the users before turning completely off as in [24]. …”

“…Making long-term segment quality plans based on: 1) future signal strength variations, and 2) spatial traffic distributions [14,24].…”

“…RA strategies that prebuffer media to users allow the BS to enter sleep modes and save energy without impacting QoE [4,24].…”

“…Users are granted more resources when at peak channel conditions to opportunistically prebuffer content [23,24]. …”