Aero: A Pluggable Congestion Control for QUIC
Bibliographic record
Abstract
QUIC is rapidly emerging as the de-facto standard for HTTP Adaptive Streaming (HAS). QUIC relies on heuristic-based congestion control algorithms which were predominantly designed for TCP and thus have poor generalizability, ultimately degrading the user's Quality of Experience (QoE). Existing learning-based solutions for TCP are not pluggable and require a lot of engineering work, impacting their integration with QUIC. To tackle these challenges, we develop Aero--- the first learning-based plug-and-play congestion control algorithm for QUIC that considers the varying network statistics and can easily be integrated with any QUIC implementation. To analyze the performance of Aero, we conduct a series of comprehensive trace-driven experiments and evaluate its efficiency not only from a congestion control perspective but also the impact it has on the client-driven adaptive bitrate scheme (ABR). Experimental results demonstrate that Aero improves VMAF by ~12% with ~65% less rebuffering for low latency live streaming sessions compared to its competitors. Moreover, Aero excels in terms of delivery rate and delay across different network conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".