In this letter, a greedy routing strategy is proposed for resource scheduling in Earth observation mission to achieve the maximum throughput and improve the energy efficiency. Resource time-expanded graph (RTEG) is introduced to depict the time-evolving topology and resources of satellite networks. Based on RTEG, an energy efficient resource allocation algorithm (EERA) is designed to reduce the total energy consumption and maximize the throughput in data transmission by properly scheduling the transceiver resource, storage resource and power resource of satellites. Simulation results show that EERA achieves preferred energy efficiency when reaching the maximum throughput.

In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) experience. The visual content between different viewpoints is highly homogenized, suggesting the possibility of good compression and encoding. However, most fixed-structure LF coding schemes are difficult to adapt to the real-time requirements of different LF applications and best-effort network conditions causing packet loss. In this paper, we propose a dynamic adaptive LF video transmission scheme that can achieve high compression and yet provide near-distortion-free LF video when the network condition is stable. Additionally, for unstable network conditions a description scheduling algorithm is proposed, which can decode the LF video with the highest possible quality even if partial data cannot be received completely and/or timely. We achieve this by designing a Multiple Description Coding (MDC) based solution to transport the LF video compressed by a Graph Neural Network (GNN) model. Experimental results show that the scheduling algorithm can improve the quality of the decoding results by 3% to 15%. Compared with other similar schemes, our system greatly improves the reliability of the video streaming system against packet loss/error and supports heterogeneous receivers.

360-degree videos, as a type of media that offers highly immersive experiences, often result in significant bandwidth waste due to incomplete views by users. This places a heavy demand on streaming systems to support high-bandwidth requirements. Recently, tile-based streaming systems combined with viewport prediction have become popular to improve bandwidth efficiency. However, since the viewport prediction is only reliable in the short term, maintaining a long buffer to avoid rebuffering is challenging. We propose JUST360 , a joint utility based two-tier 360-degree video streaming system in this paper. To better improve the accuracy of utility evaluation, a utility model incorporates image quality and prediction accuracy is proposed to evaluate the contribution of each tile so that longer buffer and bandwidth efficiency can coexist. The optimal bitrate allocation strategy is determined by using model predictive control (MPC) to dynamically select the tiles according to their characteristics. Experiments show that our method successfully achieves higher PSNR and less rebuffering. Compared with other state-of-the-art methods, our proposed method can outperform the other methods by 3%-20% in terms of QoE.

360-degree videos usually require extremely high bandwidth and low latency for wireless transmission, which hinders their popularity. A tile-based viewport adaptive streaming scheme, which involves accurate viewport prediction and optimal bitrate adaptation to maintain user Quality of Experience (QoE) under a bandwidth-constrained network, has been proposed by researchers. However, viewport prediction is error-prone in long-term prediction, and bitrate adaptation schemes may waste bandwidth resources due to failing to consider various aspects of QoE. In this paper, we propose a synergistic temporal-spatial user-aware viewport prediction scheme for optimal adaptive 360-Degree video streaming (SPA360) to tackle these challenges. We use a user-aware viewport prediction mode, which offers a white box solution for Field of View (FoV) prediction. Specially, we employ temporal-spatial fusion for enhanced viewport prediction to minimize prediction errors. Our proposed utility prediction model jointly considers viewport probability distribution and metrics that directly affecting QoE to enable more precise bitrate adaptation. To optimize bitrate adaptation for tiled-based 360-degree video streaming, the problem is formulated as a packet knapsack problem and solved efficiently with a dynamic programming-based algorithm to maximize utility. The SPA360 scheme demonstrates improved performance in terms of both viewport prediction accuracy and bandwidth utilization, and our approach enhances the overall quality and efficiency of adaptive 360-degree video streaming.

The Mars exploration mission of China named Tianwen-1 is being carried out as scheduled. The Navigation and Terrain Cameras (NaTeCam) equipped on the Zhurong Rover play an essential role in obstacle recognition. The main obstacles on the Martian surface are rocks of different sizes, which influence the path planning of Zhurong Rover in scientific exploration. Most existing semantic segmentation methods are based on the U-Net architecture with ResNet or other backbones, and features extracted by these methods lack of the long-range dependencies. To fully exploit the context information, we propose the MarsNet framework for Mars image, which combines Transformers with the convolutional neural network as the backbone, and hybrid dilated convolution (HDC) is also employed to the decoder path to help detect the huge rocks. Besides, since there are few open-source datasets for rock segmentation for Mars, we establish a segmentation dataset from Martian surface image, named TWMARS, captured by NaTeCam. Extensive experiments are conducted on TWMARS dataset, and the experimental results demonstrate that MarsNet achieves accurate rock segmentation and outperforms state-of-the-art methods. The source code is available at: https://github.com/BUPT-ANT-1007/MarsNet.