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.

Mars exploration highlights the demand for identifying Martian surface changes, which has sparked research interests in planetary surface changes detection (PSCD). However, the prevailing PSCD algorithms face significant challenges due to the sparse features, low resolution, and high noise levels of captured images data. In this paper, we propose an unsupervised model, the Dust Unsupervised Surface Tracking Network (DUSTNet), designed to track the surface changes caused by Martian dust storms. Our DUSTNet employs a network architecture with dual input branches to learn the cross-temporal complementary information from pre-time and post-time image pairs. A multi-level feature complementary fusion (MFCF) module is utilized to enhance the ability to detect subtle changes. Considering the difficulties in image registration caused by illumination variations, noise, and other factors, we design a noise-resistant module that mitigates pseudo-changes and improves the robustness of PSCD. In addition, we construct a dataset of Martian dust storms change detection based on the images captured by Moderate Resolution Imaging Camera (MoRIC) of China’s First Mars Mission TianWen-1 (The dataset is available at https://github.com/Limiyu1123/SDS). The detection performance of DUSTNet on multiple Mars surface datasets, including our Martian dust storm test set. Our model achieves improvements of 2.5% in precision, 7.55% in F1-score, 6.54% in OA, and 4.57% in Kappa over the state-of-the-art model.

Depth estimation plays a great potential role in obstacle avoidance and navigation for further Mars exploration missions. Compared to traditional stereo matching, learning-based stereo depth estimation provides a data-driven approach to infer dense and precise depth maps from stereo image pairs. However, these methods always suffer performance degradation in environments with sparse textures and lacking geometric constraints, such as the unstructured terrain of Mars. To address these challenges, we propose M³Depth, a depth estimation model tailored for Mars rovers. Considering the sparse and smooth texture of Martian terrain, which is primarily composed of low-frequency features, our model incorporates a convolutional kernel based on wavelet transform that effectively captures low-frequency response and expands the receptive field. Additionally, we introduce a consistency loss that explicitly models the complementary relationship between depth map and surface normal map, utilizing the surface normal as a geometric constraint to enhance the accuracy of depth estimation. Besides, a pixel-wise refinement module with mutual boosting mechanism is designed to iteratively refine both depth and surface normal predictions. Experimental results on synthetic Mars datasets with depth annotations show that M³Depth achieves a 16% improvement in depth estimation accuracy compared to other state-of-the-art methods in depth estimation. Furthermore, the model demonstrates strong applicability in real-world Martian scenarios, offering a promising solution for future Mars exploration missions.

The rapid expansion of blockchain-based digital asset trading raises new challenges in security, privacy, and efficiency. Although traditional attribute-based encryption (ABE) provides fine-grained access control, it imposes considerable computational overhead and introduces additional vulnerabilities when decryption is outsourced. To address these limitations, we present EBODS, an efficient outsourced decryption framework that combines an optimized ABE scheme with a decentralized blockchain layer. By applying policy matrix optimization and leveraging edge decryption servers, EBODS reduces the public key size by 8% and markedly accelerates computation. Security analysis confirms the strong resistance of EBODS to collusion attacks, making it suitable for resource-constrained digital asset platforms.