Minimizing Age of Semantic Information for Analytics-Oriented Video Streaming Systems
Bibliographic record
Abstract
Video streaming systems are critical for intelligent applications to transmit video data from end devices to servers for real-time analysis. In contrast to traditional human-centric streaming systems, which prioritize user-perceived metrics, machine-centric streaming systems are designed to continuously provide fresh and accurate information for analytics purposes. Although numerous studies have investigated policies to optimize streaming performance, most of them employ the segment-by-segment streaming framework from human-centric systems. Through comprehensive theoretical analysis and experimentation, we uncover that the segmented streaming approach is sub-optimal for machine-centric streaming systems compared to the straightforward frame-by-frame streaming approach. Furthermore, instead of relying on conventional frame-level metrics, we introduce a novel metric called the Age of Semantic Information (AoSI) to evaluate the performance of analytics-oriented streaming systems. This metric balances the quantity and timeliness of the semantic information. Consequently, we propose a compression ratio adaption method tailored to optimize AoSI performance for frame-by-frame streaming systems. This method leverages a deep learning (DL)-based predictor to discover the dynamic, latent relationships between compression and inference accuracy. Evaluated on actual streaming prototypes and real-world datasets, our method significantly surpasses both segmented and frame-by-frame baseline methods in terms of worst-case and average AoSI performance.
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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 | 0.014 |
| 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 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".