Soft transmission of 3D video for low power and low complexity scenario

Li, Ao; Yang, Taihai; Wu, Wenxin; Luo, Lei

doi:10.1016/j.dcan.2022.04.024

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…When the channel Signal-to-Noise Ratio (SNR) deteriorates below a certain threshold, the channel noise and impairments might result disastrous errors for entropy decoding leading to a considerable degeneracy of the received image fidelity, this is known as the ''cliff effect'' [11]. Beyond this threshold, the performance reaches its bottleneck, and the received image quality remains constant [12], unless an adaptive rate control of source and channel coding is executed according to the channel variations [13]. To resolve this problem, Joint Source Channel Coding (JSCC) and adaptive rate channel coding are introduced.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Rate Polar Code for DCT-Based Digital Image Transmission

2023

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Polar code is a recent forward error correction technique that is characterized by its low encoding/decoding complexity and good error performance. In this paper, a complete image transmission system (transmitter and receiver) is proposed to improve the image quality over different types of channels such as Gaussian, Rayleigh and Rician channels. The proposed system is based on encoding the most significant quantized Discrete Cosine Transform (DCT) coefficients of the digital image using adaptive rate polar coding, by changing the number of frozen bits depending on the number of significant DCT coefficients that depend on the channel quality. Simulation results reveal that the proposed system achieves better performance compared to the coded digital transmission schemes in terms of the Structural SIMilarity (SSIM) index and the Peak Signal to Noise Ratio (PSNR) for different sizes of images. Moreover, it is not subject to ''cliff effect'' and exhibits improvement in image quality according to the channel quality.INDEX TERMS Adaptive rate, cliff effect, DCT, frozen bits, image transmission, polar code.

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Section: Introductionmentioning

confidence: 99%

Adaptive Rate Polar Code for DCT-Based Digital Image Transmission

2023

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“…Unfortunately, most of the existing scheduling efforts for cross‐modal flows only focus on one or two of these metrics. Specifically, some researches focus on video and audio flows, in which References 11 and 12 use Device‐to‐Device communication to guarantee the data rate of video and audio flows, 13–15 optimize the quality of experience of video and audio flows, and References 16, 17 aim to solve the problem of minimizing the service delay. On the contrary, for haptic flows, 18,19 reduce the latency by dividing mini‐slots, while References 20–22 focus on guaranteeing the reliability of haptic flows using K‐repetition, diversity, and other methods.…”

Section: Introductionmentioning

confidence: 99%

Power control and resource allocation for return on investment maximization in cross‐modal communications

Wen

Zhang

Chen

et al. 2022

Trans Emerging Tel Tech

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Multi‐modal services will become the mainstream in future applications, including video, audio, and haptic flows. In multi‐modal services, video and audio primarily require high‐rate transmission, while haptic flow requires ultra‐high reliability and ultra‐low latency transmission, which imposes unprecedented challenges on the network. To improve the transmission efficiency of multi‐modal services, this article proposes a cross‐modal communication architecture in the cloud radio access network based on network slicing technology. First, we take the data rate and transmission latency of video, audio, and haptic flows contents as the quality of service (QoS) parameters, and send different modes of flows into varying slices for transmission. Second, to transmit the cross‐modal services efficiently while saving communication resources as many as possible, we define our optimization goal as the form of return on investment (ROI), that is, we aim to maximize the return of QoS parameters under the limited investment in communication resources. Third, we propose a two‐stage framework to solve this ROI maximization problem. In the first stage, we propose the power control gradient assisted binary search algorithm to solve the power control problem, which can achieve the optimization of data rate and transmission latency and obtain the suitable QoS. Then, in the second stage, we introduce the network slicing ant colony optimization algorithm to solve the slice resource allocation problem, and get the largest possible ROI. The simulation results verify the effectiveness of the proposed cross‐modal communication architecture, and prove the optimum of the proposed algorithm in terms of ROI compared to some existing algorithms, such as the reverse labeling Dijkstra algorithm, the delay acceptance algorithm, and the random searching algorithm.

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