Joint Power and Channel Resource Optimization in Soft Multi-View Video Delivery

Zhang, Ticao; Mao, Shiwen

doi:10.1109/access.2019.2946607

Cited by 11 publications

(11 citation statements)

References 33 publications

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“…So, 3D-CNN technique is used to compute features instead of 2D from both spatial and temporal dimensions. Although the number of trainable parameters increases with the size of the input window, thus reducing the inputs to 3D CNN models to a few contiguous video frames [51]. So, the introduced model achieves a balance between the number of used frames and the number of operating parameters to achieve the finest classification efficiency.…”

Section: B Classification Model Performancementioning

confidence: 99%

“…Due to the merits of the 3D multiview video coding (MVV) applications, in future multimedia communication, it will be recommended for wireless communication network [9]. For active transmission of 3D MVV, the improvement of encoding performance is achieved by taking into consideration the sequential and longitudinal correlations amongst frames in the same video and the merit of the inter-view matching inside different streams [10].…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System

et al. 2021

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Orbital angular momentum-shift keying (OAM-SK), which is the rapid switching of OAM modes, is vital but seriously impeded by the deficiency of OAM demodulation techniques, particularly when videos are transferred over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free space optic (FSO) communication system to enhance the reliability and efficiency of the model. To tackle the defects of the OAM-SK-FSO mechanism, two efficient deep learning (DL) techniques, namely convolution recurrent neural network (CRNN) and 3D convolution neural network (3DCNN) are used to decode OAM modes with a lower BER. Moreover, a graphics processing unit (GPU) is used to accelerate the training process with slight power consumption. The utilized datasets for OAM states are generated by applying different scenarios using trial and error method. The simulation results imply that LDPC coded VFs achieve the greatest peak signal-to-noise ratios (PSNRs) and the lowest BERs using the 16-OAM-SK model. Both 3DCNN and CRNN techniques have nearly the same performance, but this performance deteriorates in the case of larger dataset classes. Moreover, the GPU accelerates the performance by almost 67.6% and 36.9% using CRNN and 3DCNN techniques, respectively. These two DL techniques are more effective in evaluating the classification accuracy than the other traditional techniques by almost 10 − 20%.

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Section: B Classification Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System

et al. 2021

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“…A distributively executed model-free power allocation algorithm based on the DQN scheme was developed in [22], and the approach achieved a near-optimal policy. The authors in [23] proposed a multiagent DQN-based power control method for a multiuser video transmission system, and instantaneous CSI for each link was not needed in the model-free method. In [19]- [21], discrete action was used in continuous power control, and this problem also existed in [22] and [23].…”

Section: A Motivationmentioning

confidence: 99%

“…The authors in [23] proposed a multiagent DQN-based power control method for a multiuser video transmission system, and instantaneous CSI for each link was not needed in the model-free method. In [19]- [21], discrete action was used in continuous power control, and this problem also existed in [22] and [23]. The only way to reduce the quantization error is to increase the number of output neurons in the neural network, but this increases the computational complexity.…”

Section: A Motivationmentioning

confidence: 99%

Deep Deterministic Policy Gradient With Prioritized Sampling for Power Control

et al. 2020

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Reinforcement learning is a technique for power control in wireless communications. However, most research has focused on the deep Q-network (DQN) scheme, which outputs the Q-value for each discrete action, and does not match the continuous power control problem. Hence, this paper provides a deep deterministic policy gradient (DDPG) scheme for power control. A power selection policy designated an actor is approximated by a convolutional neural network (CNN), and an evaluation of a policy designated a critic is approximated by a fully connected network. These deep neural networks enable fast decision-making for large-scale power control problems. Moreover, to speed up the training process, this paper proposes a prioritized sampling technique, which samples the experiences that need to be learned with a higher probability. This paper simulates the proposed algorithm in a multiple sweep interference (MSI) scenario. The simulation results show that the DDPG scheme is more likely to achieve optimal policy than the DQN scheme. In addition, the DDPG scheme with prioritized sampling (DDPG-PS) converges faster than the DDPG scheme with uniform sampling.

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“…The proposed iterative algorithm can jointly maximize the achievable sum rate and minimize the distortion among multiple videos. In our recent work [17], a cross-layer optimization framework for softcast video transmission is developed and analyzed. Compared with physical layer-only designs, such cross layer optimization for video transmissions help users enjoy a better perceived video quality.…”

Section: Introductionmentioning

confidence: 99%

Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach

Zhang

Mao

2020

IEEE Access

Self Cite

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Device-to-device (D2D) communications have been regarded as a promising technology to meet the dramatically increasing video data demand in the 5G network. In this paper, we consider the power control problem in a multiuser video transmission system. Due to the non-convex nature of the optimization problem, it is challenging to obtain an optimal strategy. In addition, many existing solutions require instantaneous channel state information (CSI) for each link, which is hard to obtain in resource-limited wireless networks. We developed a multi-agent deep reinforcement learning-based power control method, where each agent adaptively controls its transmit power based on the observed local states. The proposed method aims to maximize the average quality of received videos of all users while satisfying the quality requirement of each user. After off-line training, the method can be distributedly implemented such that all the users can achieve their target state from any initial state. Compared with conventional optimization based approach, the proposed method is model-free, does not require CSI, and is scalable to large networks. INDEX TERMS Multiuser video transmission, multi-agent deep reinforcement learning, power control, quality of experience.

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Joint Power and Channel Resource Optimization in Soft Multi-View Video Delivery

Cited by 11 publications

References 33 publications

Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System

Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System

Deep Deterministic Policy Gradient With Prioritized Sampling for Power Control

Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach

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