Network Digital Twin-enhanced QoE Optimization for Adaptive Video Streaming in 6G IoV Networks
Bibliographic record
Abstract
Maintaining continuous quality of experience (QoE) for adaptive video streaming in internet of vehicle (IoV) networks is significantly challenging due to rapid user mobility and highly dynamic network conditions. To address this, we present a network digital twin (NDT)-enhanced QoE optimization scheme tailored for the unique demands of IoV environments. Our approach integrates a context-aware gated recurrent unit (GRU) within the NDT to proactively predict short-term bandwidth fluctuations and enable anticipatory bitrate adaptation. We introduce a user-centric QoE model that dynamically incorporates the real-time perceptual QoE factors derived from vehicular user digital twins (VUEDTs). Furthermore, we formulate a joint optimization problem that simultaneously determines optimal video bitrate and resource allocation to maximize user-specific QoE under high-mobility IoV scenario. This problem is modeled as a Markov decision process and solved using a proximal policy optimization learning framework. Simulation results show that our proposed scheme can achieve significantly improved user QoE when compared to other baseline schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".