Enhancing Holographic Video Communication with In-Network Computing: An Efficient 3C Resource Allocation Framework
Bibliographic record
Abstract
Holographic Video Communication (HVC) stands as a significant innovation in immersive technology, aiming to revolutionize human interaction by integrating lifelike three-dimensional communications into everyday experiences. Delivering real-time HVC presents challenges due to intensive computational demands and high expectations for Quality of Experience (QoE) requirements. This article presents an In-Network Computing (INC) empowered framework for resource allocation for HVC. Our framework provides guidelines and insights into optimizing the seamless integration of Communication, Computation, and Caching (3C) resources from the cloud to the edge. We present resource allocation strategies for the Service Function Chain (SFC) representing the HVC processing pipeline and highlight the advantages of incorporating parallel distributed rendering and pre-caching mechanisms. Through simulation, we demonstrate how our approach mitigates end-to-end delays and enhances frame refresh rates, significantly improving the holographic streaming experience. The results validate the effectiveness of our proposed strategies and offer valuable insights for further research into optimizing immersive communication technologies.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".