Utility-Based Rate-Controlled Parallel Wireless Transmission of Multimedia Streams with Multiple Importance Levels
Bibliographic record
Abstract
Multimedia data often have different levels of importance such that more important bits are less error-tolerant. A new rate control method for transporting such multimedia data over parallel wireless links with heterogeneous reliability is proposed. Rate-controlled parallel transmissions (RCPT) of different layers of a multimedia stream with different levels of importance over a wireless channel that support multiple links with heterogeneous reliability can improve the efficiency in resource allocation while satisfying the quality of service requirement of the multimedia connection. To exploit RCPT, we present and evaluate a novel dynamic resource allocation method that decomposes the available radio resources into multiple sets of links with different levels of reliability. We mathematically formulate a rate control problem for the flexible RCPT scheme and develop an efficient real-time resource allocation algorithm with a remarkably fast rate of convergence. Simulation results show that the proposed method improves the utility and reduces the power consumed for delivery of a multimedia stream at the required quality of service, in comparison with a previous scheme, where different layers of each multimedia class are scheduled with dependency, and two schemes that provide homogeneous high or low reliability over all parallel links.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".