Energy-Aware CPU Frequency Scaling for Mobile Video Streaming

Yi Yang, Wenjie Hu, Xianda Chen, Guohong Cao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The energy consumed by video streaming includes the energy consumed for data transmission and CPU processing, which are both affected by the CPU frequency. High CPU frequency can reduce the data transmission time but it consumes more CPU energy. Low CPU frequency reduces the CPU energy but increases the data transmission time and then increases the energy consumption. In this paper, we aim to reduce the total energy of mobile video streaming by adaptively adjusting the CPU frequency. Based on real measurement results, we model the effects of CPU frequency on TCP throughput and system power. Based on these models, we propose an Energy-aware CPU Frequency Scaling (EFS) algorithm which selects the CPU frequency that can achieve a balance between saving the data transmission energy and CPU energy. Since the downloading schedule of existing video streaming apps is not optimized in terms of energy, we also propose a method to determine when and how much data to download. Through trace-driven simulations and real measurement, we demonstrate that the EFS algorithm can reduce 30 percent of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50 percent of energy than the default Youtube app.

Original languageEnglish (US)
Article number8516361
Pages (from-to)2536-2548
Number of pages13
JournalIEEE Transactions on Mobile Computing
Volume18
Issue number11
DOIs
StatePublished - Nov 1 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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