Power Modeling for Video Streaming Applications on Mobile Devices
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we derive an accurate power model for video streaming which we condense to the essential components contributing the most to the overall power consumption. As a use case, we choose mobile devices on the receiver side performing video streaming in broadcasting or end-to-end scenarios. In modeling, we consider the complete video streaming toolchain, which mainly consists of data acquisition, video processing, display, and audio handling. We compose an overall power model with the help of models from the literature and propose a dedicated feature selection approach to reveal the most important factors related to power consumption. The resulting models achieve mean estimation errors below 7.61%. Results from feature selection indicate that the display brightness, the bitrate, and the frame rate have the highest impact on the power consumption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it