Conflux: A Multi-Homed Adaptive Bitrate Protocol for On-Site Live Video Streaming
Bibliographic record
Abstract
On-site, live video streaming is an important application that many depend on to receive entertainment and local and international news. Streaming live video over wireless (e.g., 3G, LTE, 5G) has begun to replace television production vans that use direct microwave links to the television station since they offer greater physical flexibility and have shorter setup times. In this method, several links are often aggregated together to stream video. Efficiently using multiple links for live, adaptive video streaming is challenging because a protocol must determine the amount of data to send on each link, the video bitrate, and the appropriate level of redundancy so that video frames can be delivered within the user’s strict latency requirements. In this paper, we present Conflux: a multi-homed, adaptive video bitrate protocol for live video streaming. Conflux’s main contribution is the design of a modular live video streaming platform that supports multipath scheduling, video bitrate adaption, and adaptive Forward Error Correction. Conflux presents a probabilistic link quality model that is used in conjunction with a user-specific utility function to determine the video bitrate and redundancy levels that maximize the user’s expected utility. These models are contained in separate modules which serve as building blocks to create customized, multi-homed adaptive video bitrate protocols for users with different requirements. Our experimental results show that Conflux provides more than 22% improvement in video quality over other multi-homed systems for typical, two-link network environments and as much as 108% improvement in more challenging network environments with up to five links.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".