Rate Scheduling of Multimedia Streams over ParallelWireless Data Channels with Heterogeneous Reliability
Bibliographic record
Abstract
A rate scheduling method for multimedia connections over parallel wireless channels with heterogeneous reliability is developed. Transmissions of different parts of a multimedia stream with different level of error tolerance over a wireless channel that support multiple links with heterogeneous reliability can improve the flexibility in resource allocation and increase the number of multimedia streams admitted by the system while satisfying the QoS requirement of each connection. To address this transmission scenario, we present and evaluate a novel dynamic resource-allocation method that decomposes the available resources into two sets of links, one with higher reliability (lower BER) than other, and allocates the links to the respective parts of each multimedia connection. We mathematically formulate a rate scheduling problem for the flexible transmission scenario 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 number of multimedia connections by 1.25%-34.6% according to the error rate in wireless link; the average number of multimedia connections that experience errors per frame by 1%-70%for low rate connections and by 5%-14%for high rate connections
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".