Energy-Aware CPU Frequency Scaling for Mobile Video Streaming

Wenjie Hu, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


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% of energy for the Youtube app, and the combination of our download method and EFS algorithm can save 50% of energy than the default Youtube app.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538617915
StatePublished - Jul 13 2017
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States
Duration: Jun 5 2017Jun 8 2017

Publication series

NameProceedings - International Conference on Distributed Computing Systems


Other37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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