Adaptive Multicast Streaming of Virtual Reality Content to Mobile Users
Bibliographic record
Abstract
Streaming virtual reality (VR) content is becoming increasingly popular. Advances in VR technologies now allow providing users with an immersive experience by live streaming popular events, such as the Super Bowl, in the form of 360-degree videos. Such services are highly interactive and impose substantial load on the network, especially cellular networks with inconsistent link capacities. In this paper, we perform rigorous analysis of 1300 VR head traces and propose a multicast DASH-based tiled streaming solution, including a new tile weighting approach and a rate adaptation algorithm, to be utilized in mobile networks that support multicast such as LTE. Our proposed solution weighs video tiles based on user's viewports, divides users into subgroups based on their channel conditions and tile weights, and determines the bitrate for each tile in each subgroup. Tiles in the viewports of users are assigned the highest bitrate, while other tiles are assigned bitrates proportional to the probability of users changing their viewports to include those tiles. We compare the proposed solution against the closest ones in the literature using simulated LTE networks and show that it substantially outperforms them. For example, it assigns up to 46% higher video bitrates to video tiles in the users' viewports than current approaches which substantially improves the video quality experienced by the users, without increasing the total load imposed on the network.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".