# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 1 Introduction  
Owing to the immense growth of industrial and entertainment applications and the rising need for an immersive visual experience, three-dimensional video (3 DV) has become a favored choice for many consumers. The 3D scene provides an arbitrary view of the actual scene. Adopting multi-view video plus depth (MVD) [1] to represent 3D video content efficiently has become more common. At the same time, 3D-video-based applications on mobile terminals have gained consumer attention due to the improvement in wireless communication (e.g., 5G and LTE [2]) and the existence of 3D-enabled laptops and naked-eye 3D mobile devices. However, some technical issues remain. Firstly, large amounts of computing resources are required to process a 3D video signal, as the amount of video datagrows proportionally to the number of cameras and the frame depth, which imposes grave challenges for resource-constrained mobile terminals. Secondly, the unpredicted channel condition of the wireless network poses a higher requirement for 3D video transmission than for traditional 2D video. Hence, any proposed efficient wireless transmission method must address these specific issues. In a 3D video transmission system, the transmitter implements several cameras to record texture videos and depth map frames of the 3D scene. After the receiver selects the preferred virtual viewpoint, the transmitter captures the frames of the adjacent cameras near the selected virtual points. These frames are considered reference viewpoint texture videos and depth maps for that virtual viewpoint. The captured data are source and channel encoded before transmission to the receiver. The demanded virtual viewpoint at the receiver is synthesized from the decoded texture video and depth map frames [3; 4; 5] via a technique named depth-image-based rendering (DIBR). Therefore, the virtual view quality is determined by the received texture video quality and depth map quality. Conventional video transmission over wireless networks relies on [PERSON]'s theorem [6; 7], in which source coding is separated from channel coding. Based on this strategy, optimal performance can be obtained provided that the channel condition and capacity are known before source and channel coding, in order to select the proper transmission rate adaptively [8]. However, this strategy does not suit practical wireless communication where the channel condition could vary drastically, and even the receiver is ignorant of the exact channel-state information. The video quality degrades abruptly due to channel capacity mismatch when the channel SNR falls below the acceptable threshold. Even when the channel SNR is high, the reconstruction quality will not improve, as the quantization factor in the digital video compression already determines the upper bound. This phenomenon is known as the _cliff effect_[9]. SoftCast, an uncoded video transmission technique, has been proposed to overcome conventional digital video transmission problems [10; 11; 12]. The transmitted signal is transformed into a series of real-value coefficients linearly with the pixel value in the video signal. By skipping quantization and entropy coding, such a transmission becomes lossy in nature. More importantly, the channel noise is directly mapped to the video signal reconstruction error, which makes the reconstruction quality commensurate with the channel SNR of the receiver. Thus, SoftCast tackles the cliff effect. However, when considering SoftCast for transmitting 3 DV, several challenges appear. Firstly, transmitting a large-sized video (i.e., MVD) over a wireless medium consumes substantial amounts of limited resources (power and bandwidth), mostly wasted in carrying reference viewpoints with quite similar content. Secondly, unlike 2D videos, every scene in 3 DV consists of texture and depth map frames. Both must be transmitted to the receiver to synthesize the virtual view, which is affected nonlinearly by the received texture videos and the depth maps.