Hierarchical Forwarding Resource Allocation With Proactive Frame Dropping for VR Video Streaming
Bibliographic record
Abstract
In this paper, a hierarchical forwarding resource allocation scheme with proactive frame dropping is proposed for multi-user virtual reality (VR) video streaming, aiming to enhance video quality in the presence of network congestion caused by multiple VR flows sharing a certain hop while considering the impact of network fluctuation. Firstly, we assess the queuing delay bound of frames, allowing bottleneck node to preemptively discard frames that are anticipated to expire. Secondly, we model the priority of different frames based on encoding referencing relationship and users' viewpoints, which helps to prioritize frames of each flow. Subsequently, we formulate an optimization problem to minimize the long-term quality loss caused by frame dropping, while adhering to per-flow quality assurance and total resource constraints. Considering the distinct timescales of network fluctuation and frame dropping, we propose a two-timescale hierarchical resource allocation scheme. On the long timescale, a resource allocation algorithm is designed based on queuing theory to calculate the resource demand of each flow. On the short timescale, to accommodate network fluctuations, a low-complexity heuristic algorithm is devised to adjust allocation results based on frame priority. Comprehensive simulation results validate the effectiveness of the proposed scheme in improving the quality of VR flow under diverse network conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".