Client-centric data streaming on smartphones: An energy perspective
Bibliographic record
Abstract
Todays users extensively download video files on their wireless handheld devices, namely, smartphones and tablet computers, which are inherently power constrained. The batteries on those devices barely last for 2-3 hours while downloading and playing video files. Experiments have shown that video downloads account for a large portion of the total energy cost of downloading and playing video files. In this paper, we present a novel, energy-efficient, purely client-centric video downloading algorithm with three tunable parameters: buffer size, low water mark, and socket-reading size. By means of implementation of the algorithm on a smartphone and measurements of the actual energy cost of downloading video files, we show the impacts of the three parameters on the energy cost of video downloads. By tuning the buffer size, low water mark, and socket-reading, we observed energy savings of 60%, 64%, and 63%, respectively. Armed with the insights into the process of video downloading, mobile app designers will be better positioned to fine tune their apps to reduce the energy cost of downloading large files in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".