In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we, first, propose an adaptive DVFS algorithm for energy efficient mono-core decoding of H.264 videos. The proposed solution uses metadata (normalized by MPEG) providing information about the upcoming workload. These metadata are processed within an adaptive filter to build dynamically an accurate complexity model used to calculate the minimal processor frequencies for decoding video frames while guaranteeing real time constraints. Then, we generalize the proposed DVFS to slice-based multi-threaded parallel video decoders on multi-core platforms. Our performance evaluations showed that the proposed algorithm for mono-core decoding is able to converge to an accurate complexity model (4 percent) in less than 1 second. Moreover, it is simple to implement, induces very low overhead and achieves up to 46 percent energy saving as compared to the ondemand Linux DVFS governor. On the other hand, joint use of parallelism and DVFS allows 720p software video decoding with only 17 percent more energy consumption as compared to a hardware video decoder.

This paper presents Open-PEOPLE, a hardware and software platform which aims to widen access to accurate power consumption measurement to the scientific community. The idea behind this platform is to centralize and abstract the instrumentation effort and the investment cost then allow geographically remote users to make power measurement remotely without requiring any specific costly hardware and/or software.

Mobile devices such as smart-phones and tablets are becoming the most important channel for delivering end-user Internet traffic especially multimedia content. One of the most popular multimedia application is video streaming. The video decoding process of this application is compute-intensive and is responsible of the consumption of a considerable part of the energy budget. Those mobile devices contain heterogeneous processing elements among-which we find Digital Signal Processors (DSP) and General Purpose Processors (GPP). In this context, the performance and energy estimation of those complex platforms is a difficult and time consuming task especially when considering both hardware and applicative parameters. In this paper, we propose a methodology for developing a unified high level video decoding performance and energy consumption analytical model for embedded heterogeneous platforms. This methodology is based on experimental measurements conducted on an embedded low-power platform. The developed model describes the performance and the energy consumption of H.264/AVC video decoding on both GPP and DSP in terms of video bit-rate, clock frequency and a set of comprehensive hardware and video related coefficients. It achieves a balance between a too abstract high level model and a detailed lower level one while guaranteeing a very good prediction properties (R-squared = 97%) for the tested videos. As a use case, we show that our model allows to accurately determine the bit-rate values for which video decoding on GPP is more energy-efficient than on DSP for a given platform.

Mobile devices such as smart-phones and tablets are increasingly becoming the most important channel for delivering end-user Internet traffic especially multimedia content. One of the most popular use of these terminals is video streaming. In this type of application, video decoding is considered as the most compute and energy intensive part. Some specific processing units, such as dedicated Digital Signal Processors (DSPs), are added to those devices in order to optimize the performance and energy consumption. In this context, the objective of this paper is to give a comprehensive and comparative study of the performance and energy consumption of video decoding application on embedded heterogeneous platforms containing a GPP and a DSP. To achieve this goal, a performance and energy characterization methodology for H.264/AVC video decoding is proposed. This methodology considers a large set of video coding parameters and operating clock frequencies to reflect different execution scenarios ranging from low-quality video decoding on low-end mobile phones to high-quality video decoding on tablets. The obtained results revealed that the best performance-energy trade-off highly depends on the required video bit-rate and resolution. For instance, the GPP can be the best choice in many cases due to a significant overhead in DSP decoding which may represent 30% of the total decoding energy in some cases. Some explanations about the obtained performance and overheads are given. Finally, guidelines on which processing element to choose according to video properties are also proposed.