Optimizing QoE for Video Streaming in the Metaverse: Analyzing User Interaction Behavior
Bibliographic record
Abstract
With the evolution of 6G networks, the convergence of the Metaverse and video streaming has opened new avenues for immersive user experiences. However, delivering high-quality video streaming in such an interactive and virtual environment poses significant challenges, particularly with regards to Quality of Experience (QoE). This paper investigates the impact of user interaction behaviors on video streaming performance in the Metaverse, specifically over 6G networks. Through a comprehensive analysis of the factors affecting QoE-such as latency, bandwidth, and interactive response times-this study proposes a novel QoE optimization framework. The framework integrates user interaction data with network resource allocation strategies, leveraging the capabilities of 6G, including ultralow latency and high-speed communication. Experimental results demonstrate improved QoE for Metaverse video streaming when user interaction behaviors are taken into account, providing insights for future developments in immersive media over 6G.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".