Applied Sciences (May 2022)

Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation

  • Saqr Khalil Saeed Thabet,
  • Emmanuel Osei-Mensah,
  • Omar Ahmed,
  • Abegaz Mohammed Seid,
  • Olusola Bamisile

DOI
https://doi.org/10.3390/app12105047
Journal volume & issue
Vol. 12, no. 10
p. 5047

Abstract

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During wireless video transmission, channel conditions can vary drastically. When the channel fails to support the transmission bit rate, the video quality degrades sharply. A pseudo-analog transmission system such as SoftCast relies on linear operations to achieve a linear quality transition over a wide range of channel conditions. When transmitting 3D videos over SoftCast, the following issues arise: (1) assigning the transmission power to texture and depth maps to obtain the optimal overall quality and (2) handling 3D video data traffic by dropping and re-allocating resources. This paper solves the pseudo-analog transmission resource allocation problem and improves the results by applying the optimal joint power allocation. First, the minimum and the target distortion optimization problems are formulated in terms of a power–bandwidth pair versus distortion. Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. Next, the three-dimensional target distortion problem is divided into two subproblems. In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. For the bandwidth-distortion problem, reaching a target distortion requires solving iteratively for the bandwidth resource closed form, given a predefined power. The proposed resource control scheme shows an improvement in transmission efficiency and resource utilization. At low power usage, the proposed method could achieve a PSNR gain of up to 1.5 dB over SoftCast and even a 1.789 dB gain over a distortion-resource algorithm, using less than 1.4% of the bandwidth.

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