rStream: Resilient and Optimal Peer-to-Peer Streaming with Rateless Codes
Bibliographic record
Abstract
Due to the lack of stability and reliability in peer-topeer networks, multimedia streaming over peer-to-peer networks represents several fundamental engineering challenges. First, multimedia streaming sessions need to be resilient to volatile network dynamics and node departures that are characteristic in peer-to-peer networks. Second, they need to take full advantage of the existing bandwidth capacities, by minimizing the delivery of redundant content and the need for content reconciliation among peers during streaming. Finally, streaming peers need to be optimally selected to construct high-quality streaming topologies, so that end-to-end latencies are taken into consideration. The original contributions of this paper are two-fold. First, we propose to use a recent coding technique, referred to as rateless codes, to code the multimedia bitstreams before they are transmitted over peer-to-peer links. The use of rateless codes eliminates the requirements of content reconciliation, as well as the risks of delivering redundant content over the network. Rateless codes also help the streaming sessions to adapt to volatile network dynamics. Second, we minimize end-to-end latencies in streaming sessions by optimizing towards a latency-related objective in a linear optimization problem, the solution to which can be efficiently derived in a decentralized and iterative fashion. The validity and effectiveness of our new contributions are demonstrated in extensive experiments in emulated realistic peer-to-peer environments with our rStream implementation.
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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) | 0.000 | 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 | 0.002 | 0.002 |
| 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".