Improving Streaming Capacity In Tree & Meshed Based P2P Live Streaming Systems
Bibliographic record
Abstract
The streaming capacity for a channel is defined as the maximum streaming rate that can be achieved by every user in the channel. In the thesis, we investigated the streaming capacity problem in both tree-based and mesh-based Peer-to-Peer (P2P) live streaming systems, respectively. In tree-based multi-channel P2P live streaming systems, we propose a crosschannel resource sharing approach to improve the streaming capacity. We use cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we propose a resource sharing approach to improve the streaming capacity. In mesh-based P2P streaming systems, each peer exchanges video chunks with a set of its neighbors. We formulate the streaming capacity problem into an optimal resource allocation problem. By solving the optimization problem, we can optimally allocate the link rates for each peer, thus improve the streaming capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".