<title>On meeting P2P streaming bandwidth demand with limited supplies</title>
Bibliographic record
Abstract
As a basic requirement of live peer-to-peer multimedia streaming sessions, the streaming playback rate needs to be strictly enforced at each of the peers. In real-world peer-to-peer streaming sessions with very large scales, the number of streaming servers for each session may not be easily increased, leading to a limited supply of bandwidth. To scale to a large number of peers, one prefers to regulate the bandwidth usage on each of the overlay links in an optimal fashion, such that limited supplies of bandwidth may be maximally utilized. In this paper, we propose a decentralized bandwidth allocation algorithm that can be practically implemented in peer-to-peer streaming sessions. Given a mesh P2P topology, our algorithm explicitly reorganizes the bandwidth of data transmission on each overlay link, such that the streaming bandwidth demand is always guaranteed to be met at any peer in the session, without depending on any a priori knowledge of available peer upload or overlay link bandwidth. Our algorithm is especially useful when there exists no or little surplus bandwidth supply from servers or other peers. It adapts well to time-varying availability of bandwidth, and guarantees bandwidth supply for the existing peers during volatile peer dynamics. We demonstrate the effectiveness of our algorithm with in-depth simulation studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".