Optimal Cooperative Multi-Source Multimedia Transmission Scheduling in Peer-to-Peer Networks
Bibliographic record
Abstract
In peer-to-peer (P2P) networks, multiple sources multimedia transmission is one of the key technology in peer-to-peer (P2P) networks because of simultaneous multimedia file sharing. However, allowing all available sources to transmit to the destination node may result in serious network congestion. Thus the selection of multiple sources is one of the key issues in the design of multi-source multimedia transmission systems in P2P networks. In this paper, a cooperative multi-source sender selection scheme to minimize the multimedia distortion is proposed, based on recent advances in restless bandits algorithms. The proposed sender selection scheme has an indexability property that dramatically simplifies the computation and implementation of the policy. Furthermore, centralized control point is not necessary in the proposed scheme, and senders can join and leave from the P2P network freely. Extensive simulation results show that the proposed scheme reduces the distortion significantly compared to the existing scheme.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".