Transmission Protocol Customization for Network Slicing: A Case Study of Video Streaming
Bibliographic record
Abstract
In this article, we propose a software-defined networking (SDN)-based transmission protocol (SDTP) for futuregeneration networks. Different services are supported by dedicated network slices, each of which can be operated separately with a customized transmission protocol. In particular, we focus on video streaming services supported by in-network caching functions. By exploiting the flexibility of scalable video coding (SVC) and SDN control intelligence, we present an in-network bottleneck queue management strategy in conjunction with a novel selective caching policy (SCP) for congestion detection and mitigation. Additionally, an enhanced transmission (ET) scheme is devised to improve the video user experience by opportunistically requesting cached video packets when network conditions permit. The proposed protocol can adapt to traffic dynamics and varying service requirements, and it is shown to effectively alleviate network congestion and provide a balanced user experience. Extensive simulation results are presented to validate the proposed protocol in terms of in-network queue stability and userperceived video quality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".