Analysis of peer-assisted video-on-demand systems with scalable video streams
Bibliographic record
Abstract
In recent years, peer-to-peer (P2P) and peer-assisted streaming have emerged as promising models for low-cost multimedia distribution to large scale user communities. In this paper, we study streaming of scalable video streams over these systems. Scalable video streams are composed of multiple layers and can easily be adapted according to the characteristics and needs of receivers. Thus, they can efficiently support a wide spectrum of heterogeneous peers participating in a P2P streaming system. We present an analytical model for forecasting the long-term behavior of a P2P streaming system with scalable video streams. Our analysis takes as inputs the characteristics of a dynamic P2P streaming system and the video streams. It then analytically computes the expected throughput of the streaming system and the expected video quality delivered to peers. The analysis also provides an upper bound on the maximum number of peers that can be admitted to the system at once (i.e., in flash crowd scenarios), while ensuring a certain video quality. We present a general analysis framework that can be customized to various practical P2P streaming systems with different characteristics. Then, we show the detailed analysis of a typical P2P streaming system and we explain how other systems can be analyzed using our model. We validate our analysis by comparing its results to those obtained from simulations, which confirm the accuracy of our analysis. Our analysis and simulations enable administrators of P2P streaming systems to predict the throughput and the video quality that can be delivered to users.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".