QoS-Driven Contextual MAB for MPQUIC Supporting Video Streaming in Mobile Networks
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Bibliographic record
Abstract
Video streaming performance may degrade substantially in a mobile environment due to fast-changing wireless links. On the other hand, to provide ubiquitous services, heterogeneous static and mobile access and backbone networks will be integrated in the sixth-generation (6G) systems, so mobile users can take advantage of multiple access options for better services. Multi-path transport-layer protocols like Multi-Path QUIC (MPQUIC) show promise in utilizing multiple access links to address the impact of mobility. However, the optimal link selection that aims to provide statistical QoS guarantee for video streaming in a mobile environment with both user mobility and network mobility remains an open issue. In this paper, based on a lightweight Multi-Armed Bandit (MAB) technique, we develop a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</u>oS-driven <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>ontextual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAB</u> (QC-MAB) framework for MPQUIC, which makes an intelligent access network selection and adaptively enables FEC coding to trade off delay, reliability and goodput. Extensive simulation results with ns-3 show that the proposed QC-MAB framework can outperform the state-of-the-art solutions. It achieves up to ten times lower video interruption ratio and three times higher goodput in highly dynamic mobile environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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