Building Multicast Trees for Multimedia Streaming in Heterogeneous P2P Networks
Why this work is in the frame
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Bibliographic record
Abstract
P2P networks have been proposed as a scalable, inexpensive solution to the problem of distributing multimedia content over the Internet. Since real P2P systems exhibit considerable heterogeneity in hardware, software and network connections, the design of P2P streaming networks must factor in this variation. There are two different sources of heterogeneity in P2P networks. Most existing work in the literature handle heterogeneity among receivers and requirements by the use of different multimedia encodings of the same content. In this paper we focus on the problems caused by heterogeneity in the network delays connecting receivers to the sender We assume that there is a single multicast tree and a single video stream. We propose new algorithms for building multicast trees for multimedia streaming in heterogeneous P2P networks. Our algorithms differ in the amount of communication and computational resources they require. We compare the performance (using simulations) of our algorithms with an existing Zigzag algorithm. Our results show that two of our algorithms ( FollowTree-Landmark-II algorithm and FollowTree algorithm) significantly outperform Zigzag.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it