Green metadata based adaptive DVFS for energy efficient video decoding

Abstract

International audienceWe present in this paper GM-DVFS, an adaptive DVFS scheme for energy efficient decoding of H.264 videos. GM-DVFS uses metadata (normalized by the MPEG Green Metadata standard) providing information about the upcoming workload. In the solution we propose, these metadata are processed within an adaptive filter to build dynamically an accurate video decoding complexity model. This model serves to calculate the minimal required processor frequency for decoding a video frame while guaranteeing the real time constraints. Our performance evaluations showed that the proposed algorithm is able to converge to an accurate complexity model (4%) in less than 1 second (in the worst case). Moreover, it is simple to implement (250 lines of C code) and induces very low overhead (1400 cycles/frames). On the other hand, it allows to achieve up to 46% energy saving as compared to the on demand Linux DVFS governor

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

“…In accurate estimates lead to frame drops, and sub-optimal CPU frequency/voltage levels adversely affect the overall system performance degrading the end-user's QoE in multimedia consumption. The emerging Green-MPEG specifications such as C-DVFS attempts to mitigate these challenges by incorporating decoding complexity/energy requirement metadata into the bitstreams to assist accurate frequency/voltage scaling [184].…”

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

“…The poor estimation of the complexity of subsequent frame/video segment is largely to blame in many cases, which leads to the sub-optimal selection of CPU frequencies and voltages. A recent Green-MPEG specification suggests the inclusion of codec-dynamic voltage frequency scaling (C-DVFS) metadata into the bit stream [13] to aid the frequency selection process in DVFS. However, estimating frame complexity in order to predict the operational CPU frequency still remains a challenging task.…”

“…Broadly, the state-of-the-art attempts to reduce decoding-complexity include efficient decoder implementations [5][6][7][8], dynamic alterations to the decoding process [9], dynamic voltage and frequency scaling (DVFS) methods [10][11][12][13], content adaptation based on scalable coding architectures [14][15][16], and media transcoding [17] approaches. Out of these, the latter two are encoder-side operations, and operate together with HTTP adaptive streaming solutions such as MPEG-DASH [2,18] to fetch videos of different decoding-complexities based on a device's remaining energy level.…”