Network Coding Aided Collaborative Real-Time Scalable Video Transmission in D2D Communications
Why this work is in the frame
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Bibliographic record
Abstract
There has been an increasing demand for providing real-time video streaming services in the next-generation cellular networks. To improve the quality of such services without additional infrastructure, neighboring devices can recover missing packets by using network coding aided collaborative transmission via device-to-device (D2D) communication. However, most existing work in this area had not considered the issue of how to schedule such coding aided collaborative transmissions effectively for supporting real-time scalable video applications in such environment. In this paper, we study how to improve the quality of real-time scalable video services by efficiently scheduling coding aided collaborative transmissions. We first formulate the problem of optimal collaborative transmission scheduling that determines the optimal transmitting sequence and coding pattern at each transmitting device, which is shown to be NP-hard. To address this problem, we propose a new weight function for measuring the quality of a coding pattern by considering packet recovery gain and potential video decoding gain at receivers. Based on this new weight function, we propose a low complexity centralized algorithm using global state information and an efficient distributed mechanism supporting localized operations in dynamic environment. We deduce their computational complexities. Simulation results verify that the proposed solution outperforms the representative work in the literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it