QUIC Adaptive Video Streaming Performance with 5G RAN Queue Management
Bibliographic record
Abstract
This paper presents a comprehensive analysis of the impact of queuing management in the Radio Link Control (RLC) buffer of the Next-Generation Node B (gNB) under fluctuating channel conditions. In the first part of the study, we determine the correlation between the queuing management at the RLC buffer and the application-layer Quality of Experience (QoE) at the mobile user. We further investigate the effects of active queue management (AQM) strategies within the RLC on video streaming flows transported over QUIC. Since QUIC is the prevailing protocol for HTTP Adaptive Streaming (HAS) in low-latency live (LLL) streaming mode, we consider multiple QUIC implementations and Adaptive Bitrate (ABR) schemes. Our analysis demonstrates that AQM strategies within the RLC layer substantially influence the QoE, with distinct impacts observed across different QUIC implementations and ABR schemes. These findings highlight network operators' need to implement advanced, intelligent solutions in the 5G core, extending beyond conventional RLC-layer metrics.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".