Low-Latency Network-Adaptive Error Control for Interactive Streaming
Bibliographic record
Abstract
We introduce a novel network-adaptive algorithm that is suitable for alleviating network packet losses for low-latency interactive communications between a source and a destination. Network packet losses happen in a bursty manner as well as an arbitrary manner, where the former is usually due to network congestion and the latter can be caused by unreliable wireless links. Our network-adaptive algorithm estimates in real time the best parameters of a recently proposed streaming code that corrects both arbitrary losses (which cause crackling noise in audio) and burst losses (which cause undesirable jitters and pauses in audio) using forward error correction (FEC). The network-adaptive algorithm updates the coding parameters in real time as follows: The destination estimates appropriate coding parameters based on its observed packet loss pattern and then the parameters are fed back to the source for updating the underlying code. In addition, a new explicit construction of practical low-latency streaming codes that achieve the optimal tradeoff between the capability of correcting arbitrary losses and the capability of correcting burst losses is provided. Simulation evaluations based on real-world packet loss traces reveal that our proposed network-adaptive algorithm combined with our optimal streaming codes achieves significantly higher reliability compared to uncoded and non-adaptive FEC schemes over UDP (User Datagram Protocol).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".