User Satisfaction-Oriented Video Streaming in Satellite Terrestrial Integrated Networks
Bibliographic record
Abstract
In this paper, we investigate video streaming for satellite broadcasting applications in satellite terrestrial integrated networks, employing the low complexity enhancement video coding scheme. Considering the inherent limitations of satellite broadcasting, we design a collaborative video streaming method where the base layer (BL) is transmitted via satellite links, while terrestrial links are utilized for transmitting the enhancement layer (EL). This design seamlessly integrates with existing satellite broadcasting schemes since the BL can utilize any standard video codec, while the EL significantly enhances video quality when received via terrestrial links. To accommodate dynamic network conditions, we formulate a video segment delivery problem aimed at maximizing the overall user satisfaction by determining the optimal number of video segments with ELs downloaded for each broadcasting channel. To solve the problem, we propose a group knapsack-based EL segment downloading (GKELD) algorithm, which transforms the user satisfaction maximization problem into a group knapsack problem and then solves it by devising a dynamic programming-based approach. Simulation results demonstrate that our proposed algorithm significantly outperforms benchmark methods, achieving higher overall audience satisfaction under limited network resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".