research-article

Watching the Watchers: Resource-Efficient Mobile Video Decoding through Context-Aware Resolution Adaptation

Authors:

Octavian Machidon,

Veljko PejovicAuthors Info & Claims

MobiQuitous '20: MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 168 - 176

https://doi.org/10.1145/3448891.3448948

Published: 09 August 2021 Publication History

Abstract

Mobile computing evolution is critically threatened by the limitations of the battery technology, which does not keep pace with the increase in energy requirements of mobile applications. A novel approach for reducing the energy appetite of mobile apps comes from the approximate Computing field, which proposes techniques that in a controlled manner sacrifice computation accuracy for higher energy savings. Building on this philosophy we propose a context-aware mobile video quality adaptation that reduces the energy needed for video playback, while ensuring that a user’s quality expectations with respect to the mobile video are met. We confirm that the decoding resolution can play a significant role in reducing the overall power consumption of a mobile device and conduct a user study with 22 participants to investigate how the context in which a video is played modulates a user’s quality expectations. We discover that a user’s physical activity and the spatial/temporal properties of the video interact and jointly influence the minimal acceptable playback resolution, paving the way for context-adaptable approximate mobile computing.

References

[1]

Google/Ipsos report, 2016. Average Number of Apps Installed on Smartphones. https://www.thinkwithgoogle.com/marketing-strategies/app-and-mobile/average-number-of-apps-on-smartphones/

[2]

[2] Monsoon Solutions high voltage power monitor. http://msoon.github.io/powermonitor/HVPM.html

[3]

[3] NewPipe - A libre lightweight streaming frontend for Android. GitHub repository. https://github.com/TeamNewPipe/NewPipe/

[4]

[4] VideoLAN VLC Media Player. https://www.videolan.org/vlc/

[5]

Hasnah Ahmad, Navrati Saxena, Abhishek Roy, and Pradipta De. 2018. Battery-aware rate adaptation for extending video streaming playback time. Multimedia Tools and Applications 77, 18 (2018), 23877–23908.

Digital Library

[6]

Atos and Greenspector Report. 2019. Top 30 energy consumption of the world’s most popular mobile apps.

[7]

Mark Beech. 2020. COVID-19 pushes up Internet use 70% and streaming more than 12%, first figures reveal. Forbes (2020). https://www.forbes.com/sites/markbeech/2020/03/25/covid-19-pushes-up-internet-use-70-streaming-more-than-12-first-figures-reveal

[8]

Sebastian Bosse, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek. 2016. Brain-Computer Interfacing for multimedia quality assessment. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 002834–002839.

Digital Library

[9]

Aaron Carroll, Gernot Heiser, 2010. An analysis of power consumption in a smartphone. In USENIX annual technical conference, Vol. 14. Boston, MA, 21–21.

[10]

CISCO. 2019. Cisco visual networking index: global mobile data traffic forecast update, 2017–2022. Technical Report. https://davidellis.ca/wp-content/uploads/2019/12/cisco-vni-mobile-data-traffic-feb-2019.pdf.

[11]

Barry H Cohen. 2008. Explaining psychological statistics. John Wiley & Sons.

[12]

John Elliot, Ah-Lian Kor, and Oluwafemi Ashola Omotosho. 2017. Energy consumption in smartphones: an investigation of battery and energy consumption of media related applications on Android smartphones. In International SEEDS Conference. http://eprints.leedsbeckett.ac.uk/4703/

[13]

Majed Elwardy, Hans-Jürgen Zepernick, Thi My Chinh Chu, and Veronica Sundstedt. 2019. Objective perceptual video quality prediction using spatial and temporal information differences. In 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 436–441.

[14]

Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. 2012. Neural acceleration for general-purpose approximate programs. In 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE, 449–460.

Digital Library

[15]

YouTube for Press. 2020. YouTube in numbers. Technical Report. YouTube, https://www.youtube.com/intl/en-GB/about/press/.

[16]

Khalil Ibrahim Hamzaoui, Mohammed Berrajaa, Mostafa Azizi, Giuseppe Lipari, and Pierre Boulet. 2020. Measurement-based methodology for modelling the energy consumption of mobile devices. International Journal of Reasoning-based Intelligent Systems 12, 1(2020), 4–16.

[17]

Wenjie Hu and Guohong Cao. 2017. Energy-aware CPU frequency scaling for mobile video streaming. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). 2314–2321.

[18]

International Telecommunication Union (ITU). 2008. P.910 : Subjective video quality assessment methods for multimedia applications. ITU-T Telecommunication standardization section of ITU (2008).

[19]

Wen-Yew Liang, Ming-Feng Chang, Yen-Lin Chen, and Chin-Feng Lai. 2013. Energy efficient video decoding for the android operating system. In 2013 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 344–345.

[20]

Andrea McIntosh, Safwat Hassan, and Abram Hindle. 2019. What can android mobile app developers do about the energy consumption of machine learning?Empirical Software Engineering 24, 2 (2019), 562–601.

Digital Library

[21]

Sasa Misailovic, Stelios Sidiroglou, Henry Hoffmann, and Martin Rinard. 2010. Quality of service profiling. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1. 25–34.

Digital Library

[22]

Sparsh Mittal. 2016. A survey of architectural techniques for near-threshold computing. J. Emerg. Technol. Comput. Syst. 12, 4, Article 46 (Dec. 2016), 26 pages. https://doi.org/10.1145/2821510

Digital Library

[23]

Anush Krishna Moorthy, Lark Kwon Choi, Alan Conrad Bovik, and Gustavo De Veciana. 2012. Video quality assessment on mobile devices: Subjective, behavioral and objective studies. IEEE Journal of Selected Topics in Signal Processing 6, 6 (2012), 652–671.

[24]

Candy Pang, Abram Hindle, Bram Adams, and Ahmed E Hassan. 2015. What do programmers know about software energy consumption?IEEE Software 33, 3 (2015), 83–89.

[25]

Joseph A Paradiso and Thad Starner. 2005. Energy scavenging for mobile and wireless electronics. IEEE Pervasive computing 4, 1 (2005), 18–27.

Digital Library

[26]

Veljko Pejović. 2019. Towards Approximate Mobile Computing. GetMobile: Mobile Comp. and Comm. 22, 4 (May 2019), 9–12. https://doi.org/10.1145/3325867.3325871

Digital Library

[27]

Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily Fox, and Hugo Larochelle. 2020. Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). arXiv preprint arXiv:2003.12206(2020).

[28]

Andreas Schuler and Gabriele Anderst-Kotsis. 2019. Examining the energy impact of sorting algorithms on Android: an empirical study. In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Houston, Texas) (MobiQuitous ’19). Association for Computing Machinery, New York, NY, USA, 404–413. https://doi.org/10.1145/3360774.3360808

Digital Library

[29]

Eric W.K. See-To, Savvas Papagiannidis, and Vincent Cho. 2012. User experience on mobile video appreciation: How to engross users and to enhance their enjoyment in watching mobile video clips. Technological Forecasting and Social Change 79, 8 (2012), 1484 – 1494. https://doi.org/10.1016/j.techfore.2012.03.005

[30]

Heehoon Shin and Joon-Sang Park. 2019. Reducing energy consumption of RNC based media streaming on smartphones via sampling. Multimedia Tools and Applications 78, 20 (2019), 28461–28475.

Digital Library

[31]

Wei Song, Dian Tjondronegoro, and Michael Docherty. 2011. Saving bitrate vs. pleasing users: where is the break-even point in mobile video quality?. In Proceedings of the 19th ACM International Conference on Multimedia (Scottsdale, Arizona, USA) (MM ’11). Association for Computing Machinery, New York, NY, USA, 403–412. https://doi.org/10.1145/2072298.2072351

Digital Library

[32]

Maciej Tomczak and Ewa Tomczak. 2014. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. (2014).

[33]

Ramona Trestian, Arghir-Nicolae Moldovan, Cristina Hava Muntean, Olga Ormond, and Gabriel-Miro Muntean. 2012. Quality utility modelling for multimedia applications for Android mobile devices. In IEEE international symposium on broadband multimedia systems and broadcasting. IEEE, 1–6.

[34]

Jingteng Xue and Chang Wen Chen. 2014. Mobile video perception: New insights and adaptation strategies. IEEE journal of selected topics in signal processing 8, 3 (2014), 390–401.

[35]

Ming Yan, Chien Aun Chan, André F Gygax, Jinyao Yan, Leith Campbell, Ampalavanapillai Nirmalathas, and Christopher Leckie. 2019. Modeling the total energy consumption of mobile network services and applications. Energies 12, 1 (2019), 184.

Cited By

Fabjančič MMachidon OSharif HZhao YMisailović SPejović V(2024)Mobiprox: Supporting Dynamic Approximate Computing on MobilesIEEE Internet of Things Journal10.1109/JIOT.2024.336595711:9(16873-16886)Online publication date: 1-May-2024
https://doi.org/10.1109/JIOT.2024.3365957
Baek DLim YRyoo J(2024)SenseQ: Context-Aware Video Quality Adaptation for Optimal Mobile Video Streaming in Dynamic EnvironmentsIEEE Access10.1109/ACCESS.2024.335483712(20209-20220)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3354837

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cover image ACM Other conferences

MobiQuitous '20: MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

December 2020

493 pages

ISBN:9781450388405

DOI:10.1145/3448891

Editors:
Max Mühlhäuser
Technical University of Darmstadt, Germany
,
George C. Polyzos
Athens University of Economics and Business Athens, Greece
,
Florian Michahelles
Siemens Corporation, USA
,
Alejandro Sanchez Guinea
Technical University of Darmstadt, Germany
,
Lin Wang
Vrije Universiteit (VU) Amsterdam

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2021

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Javna Agencija za Raziskovalno Dejavnost RS

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MobiQuitous '20

MobiQuitous '20: Computing, Networking and Services

December 7 - 9, 2020

Darmstadt, Germany

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Cited By

Fabjančič MMachidon OSharif HZhao YMisailović SPejović V(2024)Mobiprox: Supporting Dynamic Approximate Computing on MobilesIEEE Internet of Things Journal10.1109/JIOT.2024.336595711:9(16873-16886)Online publication date: 1-May-2024
https://doi.org/10.1109/JIOT.2024.3365957
Baek DLim YRyoo J(2024)SenseQ: Context-Aware Video Quality Adaptation for Optimal Mobile Video Streaming in Dynamic EnvironmentsIEEE Access10.1109/ACCESS.2024.335483712(20209-20220)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3354837

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