Optimal peer selection for minimum-delay peer-to-peer streaming with rateless codes
Why this work is in the frame
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Bibliographic record
Abstract
Due to the limitation of peer upload capacities and high bandwidth demand of multimedia applications, optimal peer selection to construct high-quality streaming topology represents a major challenge in peer-to-peer streaming. In this paper, we propose a fully distributed algorithm to achieve optimal peer selection and streaming rate allocation, which minimizes end-to-end latencies in the streaming sessions. We design this efficient distributed algorithm based on the solution to a linear optimization model, which optimizes towards a latency-related objective to decide the best streaming rates among peers. Combining this optimal peer selection algorithm with our coding scheme based on rateless codes, we obtain a complete, fully decentralized minimum-delay peer-to-peer streaming scheme. Our scheme is resilient to network dynamics that is characteristic in peer-to-peer networks. The validity and effectiveness of our approach are demonstrated in extensive simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it