What Makes QUIC Configurations Better for Streaming?
Bibliographic record
Abstract
QUIC has emerged as the de-facto standard for video streaming, especially with 5G enabling 4K content delivery. However, fluctuations between 4G and 5G due to congestion, mobility, or obstructions cause bandwidth variations that pose challenges for HTTP Adaptive Streaming (HAS), degrading Quality of Experience (QoE). Current QUIC implementations rely on static configurations, such as fixed pacing rates and buffer sizes, limiting adaptability to network conditions in real-time. Additionally, low server throughput combined with constrained Active Queue Management (AQM) at the router can further degrade performance if congestion control and pacing do not adjust, resulting in playback interruptions and reduced video quality. To tackle these challenges, we propose LCA-Q, a server-side learning-based Connection-Aware QUIC that leverages encrypted playback statistics alongside encrypted AQM data from the router. This approach enables real-time adjustment of transmission parameters for each connection, responding to varying client demands and network conditions. Additionally, LCA-Q can adapt its pacing rate dynamically for each connection to effectively handle highly fluctuating bandwidth. Preliminary results demonstrate that LCA-Q improves the VMAF by ~4%, QoEyin by ~41% while reducing rebuffering duration by ~8% for video-on-demand (VoD) streaming mode and improves the VMAF by ~7%, QoEyin by ~49% with comparable rebuffering for low-latency-live (LLL) streaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".