Reliable interactive video streaming in peer-to-peer networks
Bibliographic record
Abstract
Forward error correction (FEC) coding is the preferred error correction technique for interactive video streaming applications on the Internet. Because its performance is impaired by the burstiness of packet loss of Internet links, peer-to-peer (P2P) networks are often proposed to provide multiple paths between a sender and a receiver. However, peers may leave abruptly and the number of disjoint paths may be limited; it is unclear whether or when the use of P2P networks for path diversity can be justified. In this paper, we study the packet loss ratio after FEC correction when using P2P networks to provide multiple paths. We examine two situations: a sender can find enough disjoint paths, or uses a limited number of disjoint paths. We model Internet links using Markov chains, provide numerical analysis of the performance of systematic FEC codes, and verify the results by simulation. We find that although using P2P networks for path diversity often results in a lower post-FEC loss ratio, conditions apply. There exist guidelines but no simple formula to determine when to use P2P networks for path diversity and coding parameters. An application should carefully evaluate the performance gain before taking actions.
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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 | 0.006 |
| 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 | 0.002 |
| 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".