Performance Analysis of Network-Coding-Based P2P Live Streaming Systems
Bibliographic record
Abstract
Peer-to-peer (P2P) video streaming is a scalable and cost-effective technology to stream video content to a large population of users and has attracted a lot of research for over a decade now. Recently, network coding has been introduced to improve the efficiency of these systems and to simplify the protocol design. There are already some successful commercial applications that utilize network coding. However, previous analytical studies of network-coding-based P2P streaming systems mainly focused on fundamental properties of the system and ignored the influence of the protocol details. In this study, a unique stochastic model is developed to reveal how segments of the video stream evolve over their lifetime in the buffer before they go into playback. Different strategies for segment selection have been studied with the model, and their performance has been compared. A new approximation of the probability of linear independence of coded blocks has been proposed to study the redundancy of network coding. Finally, extensive numerical results and simulations have been provided to validate our model. From these results, in-depth insights into how system parameters and segment selection strategies affect the performance of the system have been obtained.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".