Proactive Energy Optimization Algorithms for Wavelet-Based Video Codecs on Power-Aware Processors

Akella, Venkatesh; Schaar, Mihaela van der; Kao, Wei-Jiun

doi:10.1109/icme.2005.1521486

Cited by 7 publications

(9 citation statements)

References 9 publications

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“…For example, in [6], the optimal action to turn on/off a disk does not change over time, but in this work optimal decisions depend explicitly on the current time and remaining energy. Quality versus resource utilization trade-offs have also been studied extensively in the area of video streaming [3,9,14,15,[18][19][20]. This is somewhat orthogonal to our work, since most streaming applications would be user-initiated and therefore not delay tolerant.…”

Section: Related Workmentioning

confidence: 93%

Markov decision process (MDP) framework for software power optimization using call profiles on mobile phones

Jung

Maker

Cheung

et al. 2010

Des Autom Embed Syst

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We present an optimization framework for delay-tolerant data applications on mobile phones based on the Markov decision process (MDP). This process maximizes an application specific reward or utility metric, specified by the user, while still meeting a talktime constraint, under limited resources such as battery life. This approach is novel for two reasons. First, it is user profile driven, which means that the user's history is an input to help predict and reserve resources for future talk-time. It is also dynamic: an application will adapt its behavior to current phone conditions such as battery level or time before the next recharge period. We propose efficient techniques to solve the optimization problem based on dynamic programming and illustrate how it can be used to optimize realistic applications. We also present a heuristic based on the MDP framework that performs well and is highly scalable for multiple applications. This approach is demonstrated using two applications: Email and Twitter synchronization with different priorities. We present experimental results based on Google's Android platform running on an Android Develepor Phone 1 (HTC Dream) mobile phone.

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Section: Related Workmentioning

confidence: 93%

Markov decision process (MDP) framework for software power optimization using call profiles on mobile phones

Jung

Maker

Cheung

et al. 2010

Des Autom Embed Syst

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“…Quality versus resource utilization trade-offs have been studied widely in the area of video streaming [10,11,16,15,1,14,6] In comparison, our work is more dynamic in nature, under the notion that we want to maximize user experience until the explicit charging time, instead of maximizing lifetime. For example, the optimal action to turn on/off disk will not change over time in [3], but will depend explicitly on the current time and remaining energy in this work.…”

Section: Related Workmentioning

confidence: 99%

“…We measured the power consumption of certain events specific to the Android G1 mobile phone by using a DC power supply (Agilent E3644A [1]) to power the phone instead of the phone's battery. We connected the power supply to the phone and a computer using the IEEE-488A General Purpose Interface Bus (GPIB).…”

Section: Device Setupmentioning

confidence: 99%

Markov decision process (MDP) framework for optimizing software on mobile phones

Cheung

Okamoto

Maker

et al. 2009

Proceedings of the Seventh ACM International Conference on Embedded Software

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We present a framework based on Markov decision process to optimize software on mobile phones. Unlike previous approaches in literature that focus on energy optimization while meeting a specific task-related time constraint, we model the desired talk-time as an explicit user given parameter and formulate the optimization of resources such as battery-life on a mobile phone as a decision processes that maximizes a user specified application specific reward or utility metric while meeting the talk-time constraint. We propose efficient techniques to solve the optimization problem based on dynamic programming and illustrate how it can be used in the context of realistic applications such as WiFi radio power optimization and email synchronization. We present a design methodology to use the proposed technique and experimental results using the Android platform from Google running on the HTC mobile phone.

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“…Cross-layer energy minimization is able to achieve significant energy saving by exploring joint design of hardware scheduling, encoder P-R-D control, and wireless transmission [37,39,40,43]. The P-R-D model links the encoder resource parameters at the application layer with other resource parameters at the physical and link layers, which enables us to develop cross-layer energy minimization schemes.…”

Section: Iv-c-a Cross-layer Energy Minimizationmentioning

confidence: 99%

“…To reduce the energy consumption of video encoders, a number of algorithms, software and hardware energy-minimization techniques, including lowcomplexity encoder design [30][31][32], low-power embedded video encoding [33,34], adaptive power control [29,35,36], and joint encoder and hardware adaptation [37,39,40] have been developed. These algorithms focus on encoder complexity (and power consumption) reduction through heuristic adaptation or control instead of systematic energy optimization.…”

Section: Power-rate-distortion Analysis For Video Encoding Energymentioning

confidence: 99%

Energy-aware portable video communication system design for wildlife activity monitoring

He¹,

Eggert²,

Cheng³

et al. 2008

IEEE Circuits Syst. Mag.

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Proactive Energy Optimization Algorithms for Wavelet-Based Video Codecs on Power-Aware Processors

Cited by 7 publications

References 9 publications

Markov decision process (MDP) framework for software power optimization using call profiles on mobile phones

Markov decision process (MDP) framework for software power optimization using call profiles on mobile phones

Markov decision process (MDP) framework for optimizing software on mobile phones

Energy-aware portable video communication system design for wildlife activity monitoring

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