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.

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.