Maximum-Utility Scheduling for Multimedia Transmission in Drive-Thru Internet
Bibliographic record
Abstract
How to support multimedia services for people on the road is a pressing issue. Relying on the vehicle-to-infrastructure (V2I) communications, the limited wireless resources and vehicle sojourn time make it quite challenging to schedule the transmissions of multiple vehicles to ensure high efficiency and quality. In this paper, the scheduling of multimedia transmissions over drive-thru Internet is investigated. A utility model is devised to map the throughput to user's satisfaction level. The objective of the scheduling problem is to maximize the total utility. Then, the optimization problem is formulated as a finite-state decision problem with the assumption that future arrival information is known, and it is solved by a searching algorithm as the benchmark. To obtain a real-time solution, a practical heuristic algorithm based on the concept of utility potential is devised. We further implemented the solution and conducted extensive simulations using NS-3, and the simulation results show that the proposed heuristic algorithm can outperform the state-of-the-art one; therefore, it can effectively make scheduling decisions to achieve both higher utility and efficiency and better fairness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".