Optimal Resource Allocation For Video Streaming Over Cognitive Radio Network Via Geometric Programming
Bibliographic record
Abstract
Cognitive Radio (CR) is a new paradigm in wireless communications to enhance utilization of limited spectrum resources. In the cognitive radio networks, each secondary user can use wireless channels for data transmission to improve the spectrum utilization. This thesis focus on the resource allocation problem for video streaming over cognitive radio networks, where secondary users and primary users transmit data simultaneously in a common frequency band. Respectively, we investigate CR in both single channel and multiple channels scenarios for single-layered and multi-layered streaming video, which is encoded into multiple layers delivered over a separate channel. Moreover, the source rate, the transmission rate, and the transmission power at each video session in each channel are jointly optimized to provide Quality of Service (QoS) guarantee to all video sessions in the secondary network. The optimization problem is formulated into a Geometric Programming (GP) problem, which can be solved efficiently. In the simulations, we demonstrate that the proposed scheme can achieve a lower Packet Loss Rate (PLR) and queuing delay, thus leading to a higher video quality for the video streaming sessions, compared to the uniform scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".