End-to-end delay control of multimedia applications over multihop wireless links
Bibliographic record
Abstract
The proliferation of multimedia applications over mobile, resource-constrained wireless networks has raised the need for techniques that adapt these applications both to clients' Quality of Service (QoS) requirements and to network resource constraints. This article investigates the upper-layer adaptation mechanisms to achieve end-to-end delay control for multimedia applications. The proposed adaptation approach spans application layer, middleware layer and network layer. In application layer, the requirement adaptor dynamically changes the requirement levels according to end-to-end delay measurement and acceptable QoS requirements for the end-users. In middleware layer, the priority adaptor is used to dynamically adjust the service classes for applications using feedback control theory. In network layer, the service differentiation scheduler assigns different network resources (e.g., bandwidth) to different service classes. With the coordination of these three layers, our approach can adaptively assign resources to multimedia applications. To evaluate the impact of our adaptation scheme, we built a real IEEE 802.11 ad hoc network testbed. The test-bed experiments show that the proposed upper-layer adaptation for end-to-end delay control successfully adjusts multimedia applications to meet delay requirements in many scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".