Reducing Power Consumption in Mobile Terminals—Video Computing Perspective

  • Martti ForsellEmail author
Part of the Computer Communications and Networks book series (CCN)


The high power consumption of video playback in mobile terminals has two major negative impacts. It limits the available operating time of the terminal devices deteriorating the user experience and contributes to the global energy consumption with negative environmental effects. Solutions to reduce the energy usage of mobile terminals include increasing the power efficiency of the terminal components, optimizing the video decoding and playback software as well as altering the quality and/or compression settings of the video data. While there exist some studies on power consumption with these means, most of them do not apply to state-of-the-art terminal hardware nor do they take a systematic approach to evaluate all available techniques. The focus of this chapter is on reducing the power consumption of mobile terminals as a part of video delivery system. For that, a wide variety of video computing-related energy-saving techniques is presented and evaluated on a line of Apple laptop mobile terminals running publicly available video playback software. The tests are done with video clips representing different kinds of video content and the effect of terminal hardware, video coding, video quality, player software, execution-environment/parameters and streaming to power consumption and data rate is measured. According to the measurements, the video computing induced consumption, total power consumption and data rate can be reduced with respect to the state-of-the-art situation in the beginning of the research by 83%, 65%, and 43%, respectively. Additional reductions can be achieved by decreasing the quality of videos, switching to smaller footprint devices, and replacing the current processors with more advanced ones.



This research was supported by the European Celtic-Plus project CONVINcE and funded by the Finnish Funding Agency for Innovation (TEKES) and VTT. I would like to thank Tuomas Nissilä and Jussi Roivainen who helped in the measurements.


  1. 1.
    Mills M (2013) The cloud begins with coal: big data, big networks, big infrastructure, and big power—an overview of the electricity used by the global digital ecosystem. Digital Power GroupGoogle Scholar
  2. 2.
    Ericsson Mobility Report (2013) Ericsson June 2013Google Scholar
  3. 3.
    Balasubramanian N, Balasubramanian A, Venkataramani A (2009) Energy consumption in mobile phones: a measurement study and implications for network applications. Paper presented at IMC’09, Chicago, Illinois, USA, 4–6 Nov 2009Google Scholar
  4. 4.
    Carroll A, Heiser G (2010) An analysis of power consumption in a smartphone. Paper presented at the 2010 USENIX annual technical conference, Boston, MA, 23–25 June 2010Google Scholar
  5. 5.
    Choi M (2013) Power performance analysis of smart device. Int J Smart Home 7:57–66CrossRefGoogle Scholar
  6. 6.
    Chen X, Chen Y, Ma Z, Fernandes F (2013) How is energy consumed in smartphone display applications? Paper presented at ACM HotMobile’13, Jekyll Island, Georgia, USA, 26–27 Feb 2013Google Scholar
  7. 7.
    Li X, Ma Z, Fernandes F (2012) Modeling power consumption for video decoding on mobile platform and its application to power-rate constrained streaming. Paper presented at 2012 IEEE visual communications and image processing, San Diego, CA, USA, 27–30 Nov 2012Google Scholar
  8. 8.
    Information technology-MPEG Systems Technologies-Part 11: Energy-Efficient Media Consumption (Green Metadata) (2015) ISO/IEC JTC1/SC29/WG11 International Standard. 23 001-11, July 2015Google Scholar
  9. 9.
    Fernandes F, Ducloux X, Ma Z, Faramarzi E, Gendron P, Wen J (2015) The Green Metadata standard for energy-efficient video consumption. IEEE Multimed Mag 22:80–87CrossRefGoogle Scholar
  10. 10.
    Ostermann J et al (2004) Video coding with H.264/AVC: tools, performance, and complexity. IEEE Circuits Syst Mag 4:7–28CrossRefGoogle Scholar
  11. 11.
    Sullivan G, Ohm J-R, Han W-J, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Video Technol 22:1649–1668CrossRefGoogle Scholar
  12. 12.
    Viitanen M, Vanne J, Hämäläinen T, Gabbouj M, Lainema J (2012) Complexity analysis of next-generation HEVC decoder. Paper presented at the 2012 IEEE international symposium on circuits and systems (ISCAS), Seoul, South Korea, 20–23 May 2012Google Scholar
  13. 13.
    Mesa M, Chi C, Schierl T, Juurlink B (2012) Evaluation of parallelization strategies for the emerging HEVC standard. Paper presented at the 2012 IEEE international conference on acoustics, speech and signal processingGoogle Scholar
  14. 14.
    Bossen F, Bross B, Sühring K, Flynn D (2012) HEVC complexity and implementation analysis. IEEE Trans Circuits Syst Video Technol 22:1685–1696CrossRefGoogle Scholar
  15. 15.
    Han B, Wang R, Wang Z, Dong S, Wang W, Gao W (2014) HEVC decoder acceleration on multi-core X86 platform. 2014 IEEE international conference on acoustic, speech and signal processing (ICASSP), Florence, Italy, 4–9 May 2014Google Scholar
  16. 16.
    Hamidouche W, Raulet M, Déforges O (2014) Multi-core software architecture for the scalable HEVC decoder. Paper presented at acoustics, speech and signal processing (ICASSP), 2014 IEEE international conference on, Florence, Italy, 4–9 May 2014Google Scholar
  17. 17.
    Hennessy J, Patterson D (2011) Computer architecture: a quantitative approach, 5th edn. Morgan KaufmannGoogle Scholar
  18. 18.
    Pamunuwa D, Zheng L-R, Tenhunen H (2003) Maximizing throughput over parallel wire structures in the deep submicrometer regime. IEEE Trans VLSI Syst 11:224–243CrossRefGoogle Scholar
  19. 19.
    Forsell M (2010) On the performance and cost of some PRAM models on CMP hardware. Int J Found Comput Sci 21:387–404MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Forsell M, Roivainen J (2015) REPLICA T7-16-128—A 2048-threaded 16-core 7-FU chained VLIW chip multiprocessor. Presented at the special session on multicore, manycore and distributed systems at the 48th asilomar conference on signals, systems, and computers, Pacific Grove, USA, 2–5 Nov 2014Google Scholar
  21. 21.
    Forsell M, Roivainen J, Leppänen V (2016) Outline of a thick control flow architecture. Presented at the 5th workshop on parallel programming models special edition on task parallelism, Marina del Rey Marriott, Los Angeles, USA, 26–28 Oct 2016Google Scholar
  22. 22.
    Forsell M, Roivainen J, Leppänen V (2016) The REPLICA on-chip network. Presented at the 2016 IEEE nordic circuits and systems conference, Copenhagen, Denmark, 1–2 Nov 2016Google Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Computing PlatformsVTTOuluFinland

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