The emergence of video streaming applications, together with the users' demand for high-resolution contents, has led to the development of new video coding standards, such as High Efficiency Video Coding (HEVC). H...
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The emergence of video streaming applications, together with the users' demand for high-resolution contents, has led to the development of new video coding standards, such as High Efficiency Video Coding (HEVC). HEVC provides high efficiency at the cost of increased complexity. This higher computational burden results in increased power consumption in current multicore servers. To tackle this challenge, algorithmic optimizations need to be accompanied by content-aware application-level strategies, able to reduce power while meeting compression and quality requirements. In this paper, we propose a machine learning-based power and thermal management approach that dynamically learns and selects the best encoding configuration and operating frequency for each of the videos running on multicore servers, by using information from frame compression, quality, encoding time, power, and temperature. In addition, we present a resolution-aware video assignment and migration strategy that reduces the peak and average temperature of the chip while maintaining the desirable encoding time. We implemented our approach in an enterprise multicore server and evaluated it under several common scenarios for video providers. On average, compared to a state-of-the-art technique, for the most realistic scenario, our approach improves BD-PSNR and BD-rate by 0.54 dB, and 8 percent, respectively, and reduces the encoding time, power consumption, and average temperature by 15.3, 13, and 10 percent, respectively. Moreover, our proposed approach enhances BDPSNR and BD-rate compared to the HEVC Test Model (HM), by 1.19 dB and 24 percent, respectively, without any encoding time degradation, when power and temperature constraints are relaxed.
It is well-known that the wider the range of extraction points a scalable bitstrearn supports, the lower the compression efficiency at these extraction points. Moreover, this compression efficiency generally varies ac...
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ISBN:
(纸本)9781424414369
It is well-known that the wider the range of extraction points a scalable bitstrearn supports, the lower the compression efficiency at these extraction points. Moreover, this compression efficiency generally varies according to what combination of scalability types are used to support this range of extraction points as specified by the encoding configuration. Hence, we propose some objective criteria as a measure of coverage, compression efficiency and rate-distortion performance of a configuration, and then present a multiple-objective optimization formulation to select the best encoding configuration for scalable video coding, given a range of bitstreams that must be supported. The method is demonstrated by experimental results.
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