A scheduling scheme for multiplexing extra streaming data into digital TV programs
Bibliographic record
Abstract
In digital TV systems, variable bitrate encoding is usually used to improve the bandwidth usage efficiency. Since the transmission channel has a fixed bandwidth, this leaves some portions of the bandwidth unoccupied. This free space can be used to transmit extra data to enhance the TV content, which can be either discrete, like text, or streaming, like video and audio. We address the problem of adding time-sensitive streaming data to a TV program. The crucial part of this problem is a scheduling algorithm that guarantees the on-time delivery of the incidental data to the decoder. We present a sophisticated time-sensitive scheduling algorithm for off-line multiplexing of TV programs and incidental streaming data. The two important features of our algorithm are: 1) it minimizes the presentation delay for incidental and main streams; 2) it minimizes the required decoder buffer size for incidental data. Comparing the experimental results of our algorithm with existing scheduling methods shows that our algorithm significantly reduces the presentation delay and the decoder buffer size for the incidental streams.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".