Low-Latency Live Video Streaming over a Low-Earth-Orbit Satellite Network with DASH
Bibliographic record
Abstract
In light of Starlink's recent rapid growth in constructing a global low-Earth-orbit satellite constellation and offering high-speed, low-latency Internet services, the implications of utilizing Starlink for low-latency live video streaming, particularly in the context of its fluctuating latency and regular satellite handovers events, remain insufficiently explored. In this paper, we conducted a thorough measurement study on the Starlink access network, examining its performance across different protocol layers and at multiple geographical installations, including locations where laser intersatellite links are utilized in practice. We performed a comprehensive latency target-based analysis of low-latency live video streaming with three state-of-the-art adaptive bitrate (ABR) algorithms in dash.js over Starlink. We presented a novel ABR algorithm designed for low-latency live video streaming over Starlink networks which leverages satellite handover patterns observed from measurements to dynamically adjust video bitrate and playback speed. The performance evaluation of the proposed algorithm was conducted using both a purpose-built network emulator and actual Starlink networks. The results demonstrate that the proposed algorithm effectively delivers a better quality of experience for low-latency live video streaming over Starlink networks, characterized by low live latency, high average bitrate, minimal rebuffering events and reduced visual quality fluctuation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".