QoE-driven cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate the problem of how to cache a set of media files with optimal streaming rates, under HTTP adaptive bit rate streaming over wireless networks. The design objective is to achieve the optimal expected QoE under a limited storage budget, which is measured by the logarithmic relation between the required bit rate and the actual streaming bit rate. We formulate the content cache management of streaming files as a constrained optimization problem. Lagrange multiplier method is employed, and we obtain the numerical solution of the optimal streaming files. Particularly, we characterize the properties of the solution, and find there is a fundamental phase change in the optimal solution as the number of cached files grows. Moreover, the simulation results indicate that with the increase of cache size, more copies of different bit rate should be cached for a better QoE. Our comprehensive investigation reveals insightful guidelines to provide HTTP ABR streaming services over wireless 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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