Cross-Layer Resource Allocation for Scalable Video Over OFDMA Wireless Networks: Tradeoff Between Quality Fairness and Efficiency
Bibliographic record
Abstract
This work addresses the tradeoff between quality fairness and system efficiency for scalable video delivery to multiple users over OFDMA wireless networks. We consider a cross-layer optimization framework seeking to maximize the sum-PSNR corresponding to average user rates, subject to relaxed PSNR-fair constraints. More specifically, a pure quality-fairness (PF) problem is solved first to determine the maximum PSNR value obtained by imposing the same PSNR level to all users. Next the constraints in the PF problem are relaxed by allowing the relative difference between the PSNR of each video and the PF PSNR value to be within some range [0, σ]. Thus, the parameter σ controls the tradeoff between quality fairness and system efficiency. The PF problem is equivalent to the quality fairness problem proposed by Cical`o and Tralli, which was solved using a vertical decomposition approach. Further, we convert the optimization problem with the relaxed fairness constraints into a convex problem and solve it using established techniques. Our simulation results show that by varying the value of σ, a wide range, densely populated, of tradeoff points between quality fairness and efficiency can be achieved. Additionally, a subjective quality assessment reveals that while the maximum efficiency scheme (ME), i.e., when σ = ∞, may compromise the quality of the high demanding videos, the PF scheme may sacrifice the quality of the low demanding videos. On the other hand, by providing a trade-off between PF and ME, the proposed scheme has the potential of finding a middle ground where all users are satisfied.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".