R2: Random Push with Random Network Coding in Live Peer-to-Peer Streaming
Bibliographic record
Abstract
In information theory, it has been shown that network coding can effectively improve the throughput of multicast communication sessions in directed acyclic graphs. More practically, random network coding is also instrumental towards improving the downloading performance in BitTorrent-like peer-to-peer content distribution sessions. Live peer-to-peer streaming, however, poses unique challenges to the use of network coding, due to its strict timing and bandwidth constraints. In this paper, we revisit the complete spectrum in the design space of live peer-to-peer streaming protocols, with a sole objective of taking full advantage of random network coding. We present R2, our new streaming algorithm designed from scratch to incorporate random network coding with a randomized push algorithm. R2is designed to improve the performance of live streaming in terms of initial buffering delays, resilience to peer dynamics, as well as reduced bandwidth costs on dedicated streaming servers, all of which are beyond the basic requirement of stable streaming playback. On an experimental testbed consisting of dozens of dual-CPU cluster servers, we thoroughly evaluate R2with an actual implementation, real network traffic, and emulated peer upload capacities, in comparisons with a typical live streaming protocol (both without and with network coding), representing the current state-of-the-art in real-world streaming applications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".