TY - JOUR
T1 - Quality of Experience Optimization for Real-Time XR Video Transmission With Energy Constraints
AU - Pan, Guangjin
AU - Xu, Shugong
AU - Zhang, Shunqing
AU - Chen, Xiaojing
AU - Sun, Yanzan
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - Extended Reality (XR) is an important service in the 5G network and in future 6G networks. In contrast to traditional video on demand services, real-time XR video is transmitted frame-by-frame, requiring low latency and being highly sensitive to network fluctuations. In this paper, we model the quality of experience (QoE) for real-time XR video transmission on a frame-by-frame basis. Based on the proposed QoE model, we formulate an optimization problem that maximizes QoE with constraints on wireless resources and long-term energy consumption. We utilize Lyapunov optimization to transform the original problem into a single-frame optimization problem and then allocate wireless subchannels. We propose an adaptive XR video bitrate algorithm that employs a Long Short Term Memory (LSTM) based Deep Q-Network (DQN) algorithm for video bitrate selection. Through numerical results, we show that our proposed algorithm outperforms the baseline algorithms, with the average QoE improvements of 0.04 to 0.46. Specifically, compared to baseline algorithms, the proposed algorithm reduces average video quality variations by 29% to 50% and improves the frame transmission success rate by 5% to 48%.
AB - Extended Reality (XR) is an important service in the 5G network and in future 6G networks. In contrast to traditional video on demand services, real-time XR video is transmitted frame-by-frame, requiring low latency and being highly sensitive to network fluctuations. In this paper, we model the quality of experience (QoE) for real-time XR video transmission on a frame-by-frame basis. Based on the proposed QoE model, we formulate an optimization problem that maximizes QoE with constraints on wireless resources and long-term energy consumption. We utilize Lyapunov optimization to transform the original problem into a single-frame optimization problem and then allocate wireless subchannels. We propose an adaptive XR video bitrate algorithm that employs a Long Short Term Memory (LSTM) based Deep Q-Network (DQN) algorithm for video bitrate selection. Through numerical results, we show that our proposed algorithm outperforms the baseline algorithms, with the average QoE improvements of 0.04 to 0.46. Specifically, compared to baseline algorithms, the proposed algorithm reduces average video quality variations by 29% to 50% and improves the frame transmission success rate by 5% to 48%.
KW - Adaptive bitrate
KW - QoE
KW - reinforcement learning
KW - wireless extended reality
UR - http://www.scopus.com/inward/record.url?scp=85198293744&partnerID=8YFLogxK
U2 - 10.1109/TVT.2024.3404266
DO - 10.1109/TVT.2024.3404266
M3 - Article
AN - SCOPUS:85198293744
SN - 0018-9545
VL - 73
SP - 15883
EP - 15888
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 10
ER -