Unequal error protection rateless coding design for multimedia multicasting
Why this work is in the frame
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Bibliographic record
Abstract
We study the design and optimization of unequal error protection (UEP) rateless codes for scalable multimedia multicasting. We formulate two general problems of optimizing UEP rateless code for multimedia multicasting to heterogenous users: one focusing on providing guaranteed quality of service (QoS) and the other focusing on providing best-effort QoS. A random interleaved rateless encoder design is proposed. Unlike previous designs, existing standardized raptor codes can be directly applied to this design without degrading performance. For each problem, optimal layer selection parameters are obtained either analytically or numerically. Numerical results demonstrate that the proposed optimized random interleaved UEP rateless code outperforms non-optimized rateless codes and recently proposed UEP rateless codes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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