Optimal resource allocation for video communication over distributed systems
Bibliographic record
Abstract
Many multimedia applications involve real-time video communication over distributed systems, in which there is no centralized controller. Examples of such distributed systems are peer-to-peer (P2P) networks, wireless ad hoc networks, and wireless sensor networks. In this paper, we provide a review of recent advances on optimal resource allocation for video communication over some major distributed systems including P2P streaming systems, wireless ad hoc networks, and wireless visual sensor networks. In P2P streaming systems, we review the scheduling optimization problem, streaming capacity problem, routing optimization problem, and the prefetching optimization problem. In wireless ad hoc networks, we present the routing optimization problem, joint optimization of the source rate and the routing scheme, joint optimization of sender selection and the routing scheme, and joint optimization of the source rate, the routing scheme and the power. In wireless visual sensor networks, we discuss the network lifetime maximization problem, optimal power allocation, maximization of accumulative visual information (AVI). Illustrative simulation results are provided to demonstrate the performance improvement brought by the optimal resource allocation in the distributed systems. Finally, we give our vision on the future work in the area of video communication over distributed systems.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".