Asynchronous DRL-based Bitrate Selection for 360- Degree Video Streaming over THz Wireless Systems
Bibliographic record
Abstract
360° videos demand substantial bandwidth to deliver an immersive viewing experience to users. In wireless networks, this high data rate demand can be accommodated by utilizing the terahertz (THz) frequency band. However, THz band communications are susceptible to self-blockage. To ensure reliable transmission, this paper studies the streaming of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$360^{\circ}$</tex> videos over THz wireless systems using multiple multi-antenna access points (APs). Users' requests for video tiles give rise to an optimization problem that involves asynchronous bitrate selection for those tiles and beamforming design for the APs. We formulate this problem as a macro-action decentralized partially observable Markov decision process (MacDec-POMDP). To efficiently tackle this problem for multiple users, we propose an asynchronous deep reinforcement learning (DRL) algorithm using a multi-agent actor-critic method to determine the bitrate selection policy. The APs' beamforming is determined by solving an optimization problem using the weighted minimum mean square error (WMMSE) algorithm. Results show that our proposed approach provides a higher average quality of experience (QoE) for the users when compared with two benchmark algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".