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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".