M-IIoT System for Reducing Bandwidth Costs in Cloud-Hybrid Multimedia Pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advent of cloud computing and 5G, Outside Broadcasting (OB) vehicles and mobile multimedia capturing workflows are moving towards cloud-hybrid approaches for processing and distribution. These evolving multimedia pipelines are championed by broadcasters for their software-enabled flexibility and reduced capita costs. However, they are subject to the Internet's indeterministic traffic and path conditions that undermine the performance of the new workflows. In this paper, a novel Multimedia Industrial Internet-of- Things (M-IIoT) real-time lossless compression scheme is proposed to reduce the bandwidth request to Internet service providers. The proposed scheme is designed to losslessly compress User Datagram Protocol (UDP)-based payloads generated by real-time broadcasts prior to flight over the Internet and similar networks. As the total amount of data exchanged decreases due to the compression process, the bandwidth utilization by intermediate nodes to transmit and retransmit lost packets due to the Internet's environment is reduced due to smaller over-all packet sizes and short average wait times. Using D/M/1 queues, the expected cost savings enabled by the proposed scheme are approximated and are found as a function of transmission bitrates. The proposed solution is simulated using Network Simulator 3 (NS3) with FFMPEG-based source nodes as well as implemented on in-the-field playout devices. The results show a decrease in bandwidth utilization and transmission costs in error free networks as well as networks with induced errors of a minimum of 40.0% and a maximum of 97.0%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it