CRoss-MODAL Communications For Holographic Video Streaming
Bibliographic record
Abstract
Holographic video, coupled with spatial audio and haptic feedback, is gradually emerging as an enabling medium to deliver interactive and immersive experiences. However, streaming holographic video over wireless networks remains a significant challenge due to high bandwidth demand, resource competition among different modalities, and the dynamic nature of network conditions. To tackle these challenges, we introduce the cross-modal communications paradigm in this work, which exploits potential correlations among video, audio, and haptic modalities to enhance holographic video streaming from both encoding and transmission perspectives. Specifically, we first propose a cross-modal visual saliency prediction method for holographic video compression, leveraging spatiotemporal information provided by spatial audio and haptic feedback to improve prediction accuracy. Then, by exploring semantic correlations between video and haptic modalities, we establish a perceptionIossless haptic coding architecture for extreme haptic compression to alleviate resource competition with holographic video streams. Finally, based on transmission priority settings of video tiles guided by audio and haptic streams, we develop an efficient holographic video rate adaption scheme under time-varying network conditions to consistently ensure immersive experiences. Numerical results validate the benefits of cross-modal communications for holographic video streaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".