Conference Paper

Using offline bitstream analysis for power-aware video decoding in portable devices.

DOI: 10.1145/1101149.1101209 Conference: Proceedings of the 13th ACM International Conference on Multimedia, Singapore, November 6-11, 2005
Source: DBLP

ABSTRACT Dynamic voltage/frequency scheduling algorithms for multimedia applications have recently been a subject of intensive research. Many of these algorithms use control-theoretic feedback techniques to predict the future execution demand of an application based on the demand in the recent past. Such techniques suffer from two major disadvantages: (i) they are computationally expensive, and (ii) it is difficult to give performance or quality-of-service guarantees based on these techniques (since the predictions can occasionally turn out to be incorrect). To address these shortcomings, in this paper we propose a completely new approach for dynamic voltage and frequency scaling. Our technique is based on an offline bitstream analysis of multimedia files. Based on this analysis, we insert metadata information describing the computational demand that will be generated when decoding the file. Such bitstream analysis and metadata insertion can be done when the multimedia file is being downloaded into a portable device from a desktop computer. In this paper we illustrate this technique using the MPEG-2 decoder application. We show that the amount of metadata that needs to be inserted is a very small fraction of the total size of the video clip and it can lead to significant energy savings. The metadata inserted will typically consist of the frequency value at which the processor needs to be run at different points in time during the decoding process. Lastly, in contrast to runtime prediction-based techniques, our scheme can be used to provide performance and quality-of-service guarantees and at the same time avoids any runtime computation overhead.

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    ABSTRACT: This article introduces a novel approach to energy-efficient media stream decoding that is based on the notion of media stream similarity. The key idea is that platform-independent scenarios with similar decoding complexity can be identified within and across media streams. A device that decodes a media stream annotated with scenario information can then adjust its processor clock frequency and voltage level based on these scenarios for lower energy consumption. Our evaluation, done using the H.264 AVC decoder and 12 reference video streams, shows an average energy reduction of 44% while missing less than 0.2% of the frame deadlines using scenario-driven video decoding. An additional application of scenario-based media stream annotation is to predict required resources (compute power and energy) for consuming a given service on a given device. Resource prediction is extremely useful in a client-server setup in which the client requests a media service from the server or content provider. The content provider (in cooperation with the client) can then determine what service quality to deliver, given the client's available resources. Scenario-aware resource prediction can predict (compute power and energy) consumption with errors less than 4% (and an overall average 1.4% error).
    ACM Transactions on Embedded Computing Systems 03/2012; 11(1):1-25. DOI:10.1145/2146417.2146419 · 0.68 Impact Factor

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