A scheduling scheme for multiplexing of VBR sources in digital TV systems
Bibliographic record
Abstract
Digital TV transmission systems allow a transmission channel to be shared by a number of sources. In order to improve the bandwidth utilization, variable bit rate encoding and statistical multiplexing techniques are usually used. However, the channel sharing requires a careful scheduling method for multiplexing. This is because the video and audio materials have to be presented at the receivers at specific points in time. In this paper, we present a novel scheduling scheme for statistical multiplexing of VBR sources. Our method is sensitive to the timing requirements of the sources and sends the packets as close to their transmission deadlines as possible. The advantages of our method are: (1) it decreases the broadcast deadline violation probability (or improves the bandwidth utilization), (2) it minimizes the delay and delay jitter of packets and (3) it generates a transport stream compliant with all the standard TV receivers. Simulations were conducted to compare our algorithm with the first-come-first-serve scheduling method. The results show that our algorithm significantly reduces both the percentage of dropped packets (by 35%-50%) and the average packet delay.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".