Adaptive Digital Twin-Assisted 3C Management for QoE-Driven MSVS: A GAI-Based DRL Approach
Bibliographic record
Abstract
Communication, computing, and buffer control (3C) management is essential to enhance quality-of-experience (QoE) in multicast short video streaming (MSVS). The existing 3C management schemes mainly rely on static data processing methods and a general QoE model, which may not efficiently improve QoE when users’ swipe behaviors exhibit distinct spatiotemporal differences. In this paper, we propose an adaptive digital twin (DT)-assisted 3C management scheme to enhance QoE in MSVS. Particularly, DTs consist of user status data and data-based models, which can update multicast groups and abstract users’ swipe features. An adaptive DT management mechanism is developed to adapt to users’ swipe behavior dynamics. Then, a fine-grained QoE model is established by considering the impact of resource constraints and DT model accuracy, leading to accurate buffer control. Finally, a joint optimization problem of 3C management is formulated to maximize long-term QoE. To efficiently solve this problem, a diffusion-based deep reinforcement learning (DRL) algorithm is proposed, which utilizes the denoising technique to improve the action exploration capabilities of DRL. Simulation results based on a real-world dataset demonstrate that the proposed DT-assisted 3C management scheme outperforms benchmark schemes in terms of QoE, with improvements of 18.4% and 20.5% under low and high user dynamics, respectively.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".