Mobile Streaming of Live 360-Degree Videos
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
Live streaming of immersive multimedia content, e.g., 360-degree videos, is getting popular due to the recent availability of commercial devices that support interacting with such content such as smartphones/tablets and head-mounted displays. Streaming live content to mobile users using individual connections (i.e., unicast) consumes substantial network resources and does not scale to large number of users. Multicast, on the other hand, offers a scalable solution but it introduces multiple challenges, including handling user interactivity, ensuring smooth quality, conserving the energy of mobile receivers, and achieving fairness among users. We propose a new solution for the problem of live multicast streaming of 360-degree videos to mobile users, which addresses the aforementioned challenges. The proposed solution, referred to as VRCast, is designed for cellular networks that support multicast, such as LTE. We show through trace-driven simulations that VRCast outperforms the closest algorithms in the literature by wide margins across several performance metrics. For example, compared to the state-of-the-art, VRCast improves the viewport quality by up to 2.5 dB. We have implemented VRCast in an LTE testbed to show its practicality. Our experimental results show that VRCast ensures smooth video quality and saves energy for mobile devices.
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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 | 0.001 | 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