CAQ: Connection-Aware Adaptive QUIC Configurations for Enhanced Video Streaming
Bibliographic record
Abstract
QUIC is rapidly emerging as the de facto standard for video streaming, particularly with the advent of 5G technology enabling the delivery of high resolution 4K content. However, sudden fluctuations in network capacity between 4G and 5G networks can occur due to various factors, such as network congestion, user mobility, or physical obstructions, leading to rapid changes in available bandwidth, which pose significant challenges for HTTP Adaptive Streaming (HAS) sessions that rely on more stable data rates for smooth playback, often resulting in interruptions and degraded Quality of Experience (QoE). Furthermore, current QUIC implementations are characterized by static configuration parameters, including constant pacing rates and static buffer sizes. This rigidity limits the server's ability to adapt dynamically to the client's real-time network conditions, making it challenging to prevent playback stalls and improve user QoE. To address these issues, we propose Connection-Aware QUIC (CAQ), which utilizes encrypted client playback statistics to dynamically adjust server parameters and implement an adaptive pacing rate. This approach aims to optimize streaming performance and enhance user experience, even in highly fluctuating network conditions. To evaluate the performance of CAQ, we conduct a series of trace-driven experiments across two key streaming modes: Video-on-Demand (VoD) and Low-Latency Live (LLL). Experimental results demonstrate that CAQ improves the VMAF by ~4%, QoEyin by ~41% while reducing rebuffering duration by ~8% for VoD mode and improves the VMAF by ~7%, QoEyin by ~49% with comparable rebuffering for LLL mode.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".