Live peer-to-peer streaming with scalable video coding and networking coding
Bibliographic record
Abstract
We present the design of a peer-to-peer (P2P) live streaming system that uses scalable video coding as well as network coding. The proposed design enables flexible customization of video streams to support heterogeneous receivers, highly utilizes upload bandwidth of peers, and quickly adapts to network and peer dynamics. Our design is simple and modular. Therefore, other P2P streaming systems could also benefit from various components of our design to improve their performance. We conduct an extensive quantitative analysis to demonstrate the expected performance gain from the proposed design. Our analysis uses actual scalable video traces and realistic P2P streaming environments with high churn rates, heterogeneous peers, and flash crowd scenarios. Our results show that the proposed system can achieve: (i) significant improvement in the visual quality perceived by peers (several dBs are observed), (ii) smoother and more sustained streaming rates, (iii) higher streaming capacity by serving more requests from peers, and (iv) more robustness against high churn rates and flash crowd arrivals of peers. This paper shows that the integration of network coding and scalable video coding in P2P live streaming systems yields better performance than current systems that use single-layer streams and proposed systems that use either network coding alone or scalable video coding alone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".