Toward Requested VR QoE Using Multi-Objective Reinforcement Learning
Bibliographic record
Abstract
Virtual reality (VR) has been viewed as an important application in the coming wireless networks. Nevertheless, it requires both broad bandwidth and low-latency transmission, which places significant pressure on limited wireless resources. Aiming for resource savings while satisfying the various QoE requirements, based on dedicated formulating VR QoE, i.e., the mean opinion score (MOS) of video quality and motion-to-photon (MTP) delay, a QoE-steering (QoE-STR) multi-objective reinforcement learning algorithm is proposed to jointly optimize the field of view (FoV) redundancy, provided video quality, computing resources, and communication resources. The algorithm is tailored to distinct user preferences through multiple basic policies. Then it introduces a QoE-STR policy selection mechanism based on the direction vector toward the requested QoE, making users' achieved QoE approach their respective requested QoE. To reduce the computing complexity, we further develop a distributed QoE-STR (Dist. QoE-STR) algorithm, which can independently allocate wireless resources to each user while guaranteeing the network's overall resource constraints. Simulation results demonstrate the effectiveness of both proposed algorithms and the Dist. QoE-STR performs better since it can independently design the direction vector for each user.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".