Energy-efficient video streaming from high-speed trains
Bibliographic record
Abstract
The problem of streaming packetized media has been intensively studied for a long time. In this paper, we revisit this problem in the high-speed railway context, where passengers encode and upload videos through increasingly powerful smartphones. The challenge is highlighted by the fast changing channel conditions in high-speed trains and the limited battery of cell phones. Inspired by the unique spatial-temporal characteristics of wireless signals along high-speed railways, we propose a novel energy-efficient and rate-distortion optimized approach for video streaming. Our solution effectively predicts the signal strength through its spatial-temporal periodicity in this new application scenario. It then smartly adjusts the GOF budget, schedules the video transmission to achieve graceful rate-distortion performance and yet conserves the energy consumption. Performance evaluation based on simulated railway scenarios and H.264 video traces demonstrates the effectiveness of our solution and its superiority as compared to existing solutions.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".