PulseQUIC: Enhancing QUIC-Based Video Streaming through DRL-Guided Adaptive Pacing
Bibliographic record
Abstract
The adoption of QUIC for HTTP Adaptive Streaming (HAS), coupled with the rise of 5G, enables seamless 4K video delivery. However, it also calls for smarter congestion control and bitrate adaptation to maintain high Quality of Experience (QoE). While QUIC's user-space architecture supports easy integration of client-side adaptive bitrate (ABR) algorithms, server-side learning-based congestion control---originally designed for TCP---remains difficult to integrate due to its reliance on kernel-level changes. To address these challenges, we propose PulseQUIC- a cross-layer learning-based pacing mechanism that combines server-side transport metrics with real-time client feedback. PulseQUIC dynamically adjusts the pacing rate based on current network conditions and can be seamlessly integrated into any QUIC implementation or congestion control algorithm, offering significant performance gains. Experimental results demonstrate that PulseQUIC increases the client-side VMAF by ~33% while reducing server-side RTT by ~30% for low-latency live (LLL) streaming mode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".