Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation
Bibliographic record
Abstract
Multi-channel Peer-to-Peer (P2P) Video-on-Demand (VoD) systems can be categorized into independent-channel P2P VoD systems and correlated-channel P2P VoD systems. Streaming capacity for a channel is defined as the maximal streaming rate that can be received by every user of the channel. In this paper, we study the streaming capacity problem in multi-channel P2P VoD systems. In an independent-channel P2P VoD system, there is no resource correlation among channels. Therefore, we can find the average streaming capacity for the independent-channel P2P VoD system by finding the streaming capacity for each individual channel, respectively. We propose a distributed algorithm to solve the streaming capacity problem for a single channel in an independent-channel P2P VoD system. The average streaming capacity for a correlated-channel P2P VoD system depends on both the intra-channel and cross-channel resource allocation. To better utilize the cross-channel resources, we first optimize the server upload allocation among channels to maximize the average streaming capacity and then propose cross-channel helpers to enable cross-channel sharing of peer upload bandwidths. We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".