PowerDoW (Power Digital Offset Weightage): Video Content-Adaptation (VCA) Profiling in Smartphone Devices for Energy Efficiency

Muhammad Hanif Jofri, Muharman Lubis, Mohd Farhan Md Fudzee, Shahreen Kasim, Mohd Norasri Ismail, Deden Witarsyah


Nowadays, the rapid enhancement of Internet connectivity and the recent progression of smartphone technologies lead to better smartphones quality towards video streaming activity. With the massive production of smartphone devices today, motivate studies of energy consumption behaviors to extend the smartphone device battery-life. Therefore, existing designs for smartphone devices occasionally lack energy-aware thus it need profiling optimization technique that reduces energy usage. Energy profiling in smartphone devices is one of the practical criteria for saving energy in smartphone devices during video streaming session. Energy efficiency features for smartphone devices, profiling and video content adaptation approach are the most critical parts for the energy-efficient while streaming in course. However, the consideration of energy-aware profiling area has not yet been discovered widely. In this case, appointing promising approaches will be used to reduce energy consumption in the smartphone devices during video streaming session. A framework called PowerDoW will be benefited towards adding energy adaptation strategies. PowerDoW framework manage and utilize system profiling status to attain the entire streaming session activity and classify the streaming video format depending on the selective video parameter. Selection of the best quality depending on low energy usage will be determined in the profiling experimentation. The experimentations are based on the Android operating system in smartphone devices—instrumentation setup testing by using PowerTutor application to measure energy consumption in real-time. The result indicates that PowerDoW framework can reduce a huge energy consumption by selecting suitable video content adaptation during video streaming session.


video content-adaptation; energy-aware; energy profiling.

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Amine, D., Elmiligi, H., Gebali, F., Energy Bugs in Mobile Devices: A Survey. IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) 2015.

Ballesteros, L.G.M., Ickin, S., Fiedler. M., Markendahl, J. Energy Saving Approaches for Video Streaming on Smartphone based on QoE Modeling. 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2016.

Dragiü, L., Hofman, D., Kovaþ, M., Žagar, M., Knezoviü., J. Power Consumption and Bandwidth Savings with Video Transcoding to Mobile Device-specific Spatial Resolution. 9th International Symposium on Communication Systems, Networks & Digital Sign (CSNDSP) 2014.

Ismail, N.M., Ibrahim, R., Fudzee, M., F. A Survey on Content Adaptation Systems towards Energy Consumption Awareness. Hindawi Publishing Corporation, Advance in Multimedia Volume 2013 - 8page, 2013.

Khan, S., Schroeder, D., Essaili, E., A., Eckehard, S. Energy-efficient and QoE-driven adaptive HTTP streaming over LTE. IEEE WCNC'14 Track 3 (Mobile and Wireless Networks) 2014.

Zheng, Q., Du, H., Li, J., Zhang, W., Li, Q. Open-LTE: An Open LTE Simulator For Mobile Video Streaming, IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 2014.

Aqil. A., Ahmed O. F.., A ∗, Srikanth., V. K., ∗, Papageorgiou. Streaming Lower Quality Video over LTE: How Much Energy Can You Save? IEEE 23rd International Conference on Network Protocols 2015.

Oche, N., Saleem, N. B. Go Green with EnVI: The Energy-Video Index. IEEE International Symposium on Multimedia 2015.

Fudzee, M., F, and Abawajy., J. A protocol for discovering content adaptation services. Algoriths and Architectures for Parallel Processing (2011). pp. 235-244.

Dung, T., N, Ho, T., B., Shimodaira., H. A Scalable Algorithm for Rule Post-pruning of Large Decision Trees. Advances in Knowledge Discovery and Data Mining (2001) pp 467-476.

Fudzee, M., F, Abawajy., J. Management of Service Level Agreement for Service-Oriented Content Adaptation Platform. Network and raffic engineering in emerging distributed computing applications (2012) pp 21-42. ISBN- 1466618892.

ITU-T Standards for Video Encoding – “Subjective video quality assessment methods for multimedia applications,” ITU-T Recommendation P.910, April (2008).

Ismail, M., N, Fudzee, M., F, Ibrahim., R., Jofri., M., H. Video Streaming Energy Consumption Analysis for Content Adaption Decision-Taking. Journal of Telecommunication, Electronic and Computer Engineering (2017). ISSN: 2180-1843.

Tarkoma. S., Sikkinen. M., Lagerspetz. E., Xiao. Y., Smartphone Energy Consumption: Modeling and Optimization. University Printing House Cambridge 2014.

DOI: http://dx.doi.org/10.18517/ijaseit.10.6.10881


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