Chance-constrained QoS satisfaction for predictive video streaming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The promising energy saving and QoS gains of Predictive Resource Allocation (PRA) techniques have recently been recognized in the wireless network research community. These gains were primarily introduced in light of perfect prediction of both mobility traces and anticipated channel rates. However, under real world considerations of prediction errors, the reported gains cannot be guaranteed and further investigation is needed. In this paper, we demonstrate the practical potential of PRA by developing a robust, probabilistic framework that guarantees QoS satisfaction for video streaming under imperfect predictions, without compromising the energy saving gains. The proposed PRA framework uses chance-constrained programming to model video streaming QoS for all users during the foreseen time horizon. Closed form solutions are developed using the Gaussian and Bernstein approximations based on the channel statistical measures. Extensive numerical simulations using a standard compliant Long Term Evolution (LTE) system are presented to examine the developed solutions, for different user mobility scenarios and target QoS levels. The results demonstrate the various design trade-offs involved toward the practical deployment of predictive video streaming in future generation networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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