Mitigating the asymmetric interests among peers in peer-to-peer video-on-demand systems
Bibliographic record
Abstract
In recent years, Peer-to-Peer (P2P) multimedia streaming has become an alternative to cable/satellite TV services. Many P2P streaming applications further provide users with DVD-like operations: play, pause, chapter selection, fast forward, and rewind. Such a real-time interactive multimedia streaming system is commonly referred to as P2P Video-on-Demand (VoD), and poses a unique challenge in providing smooth playback and seamless interaction over the Internet. In a typical P2P VoD system, a peer may play an arbitrary video segment at any time, which leads to asymmetric interests among peers. On one hand, the asymmetric interests reduce the incentives for peers to cooperate with each other. On the other hand, the asymmetric interests create more opportunities for content sharing. In this paper, we propose Coded VoD, a new approach for P2P VoD streaming, that exploits the content sharing opportunities in P2P VoD to address the incentive issue. Our experimental results show that Coded VoD achieves a smoother playback and faster responses to DVD-like operations by utilizing a network-coding-based content prefetching mechanism.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".