Auction-based P2P VoD streaming
Bibliographic record
Abstract
Real-world large-scale Peer-to-Peer (P2P) Video-on-Demand (VoD) streaming applications face more design challenges as compared to P2P live streaming, due to higher peer dynamics and less buffer overlap. The situation is further complicated when we consider the selfish nature of peers, who in general wish to download more and upload less, unless otherwise motivated. Taking a new perspective of distributed dynamic auctions, we design efficient P2P VoD streaming algorithms with simultaneous consideration of peer incentives and streaming optimality. In our solution, media block exchanges among peers are carried out through local auctions, in which budget-constrained peers bid for desired blocks from their neighbors, which in turn deliver blocks to the winning bidders and collect revenue. With strategic design of a discriminative second price auction with seller reservation, a supplying peer has full incentive to maximally contribute its bandwidth to increase its budget; requesting peers are also motivated to bid in such a way that optimal media block scheduling is achieved effectively in a fully decentralized fashion. Applying techniques from convex optimization and mechanism design, we prove (a) the incentive compatibility at the selling and buying peers, and (b) the optimality of the induced media block scheduling in terms of social welfare maximization. Large-scale empirical studies are conducted to investigate the behavior of the proposed auction mechanisms in dynamic P2P VoD systems based on real-world settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".