On Streaming Codes With Unequal Error Protection
Bibliographic record
Abstract
Error control codes for real-time interactive applications such as audio and video streaming must operate under strict delay constraints and be resilient to burst losses. Previous works have characterized optimal streaming codes that guarantee perfect and timely recovery of all source packets when the burst loss is below a certain maximum threshold. In this work, we generalize the notion of streaming codes to the unequal error protection (UEP) setting. Toward this end, we define two natural notions of streaming codes; symbol-level UEP and packet-level UEP. In the symbol-level UEP, the symbols within each source packet have varying recoverability requirements. In the packet-level UEP scenario, packets at even time slots and odd time slots have different recovery guarantees. We discuss practical motivations for both settings and develop coding schemes. We establish optimality or near-optimality guarantees through information-theoretic converse bounds. Simulations over Gilbert and Fritchman channels show that our coding schemes outperform baseline schemes over a wide range of channel parameters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".