ISPCell: An Interactive Image-Based Streaming Protocol for Wireless Cellular Networks
Bibliographic record
Abstract
Remote interaction with immersive 3D environments with acceptable level of quality of experience has become a challenging and interesting research topic. Due to the high data volume required to provide a rich experience to the user, robust and efficient wireless transport protocols have yet to be developed. On the other hand, cellular network technology has been widely deployed and is growing fast. The provision of remote interactive 3D environments over wireless cellular networks has several interesting applications, and it imposes some unsolved issues. Node mobility creates unstable bandwidth, which is a problem when providing smooth interaction to users. Although PDAs and cell phones are low resource devices, which makes it prohibitive to load and render entire virtual environments, they can still render images with relative ease. Based on this idea, this paper proposes a streaming system which relies on an image-based rendering approach, and is composed of several modules: a packetization scheme for images, an image-based rendering approach based on view morphing and its corresponding RTP payload format, and finally a bandwidth feedback mechanism and rate control. This paper illustrates some of the problems faced in this area, and provides a first step towards their solutions. We discuss our algorithms and present a set of simulation experiments to evaluate the performance of the proposed schemes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.002 |
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".