Resource allocation with service differentiation for wireless video transmission
Bibliographic record
Abstract
The next generation wireless networks need to support video traffic. A major challenge in video services over wireless networks is quality of service (QoS) provisioning. Service differentiation is a good approach for QoS provisioning to video traffic. In this paper, we propose cross-layer protocol stack architecture for wireless video transmission with service differentiation. In the cross-layer architecture, the application layer provides the lower link layer with the video compression information. Using the information, a dynamic-weight generalized processor sharing (DWGPS) discipline is proposed for the link layer resource allocation. The link layer tries to provide the application layer with a stringent delay bound and strong protection to high priority traffic in the case of resource shortage. Acceptable level of fairness can be achieved by DWGPS. A scheduling procedure for DWGPS is presented, which avoids complex per-packet virtual time calculation. It is shown that DWGPS can automatically adapt to multiuser diversity without many modifications. Simulation results demonstrate the effectiveness and efficiency of the link-layer DWGPS resource allocation.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".