Because the existing reversible information hiding algorithm has low embedding capacity and is not suitable for multispectral images, a multilevel reversible information hiding method for multispectral images based on histogram shifting of prediction error and band re-ordering was proposed. For each hiding level, firstly, the bands were re-ordered by the proposed approach, then, an adaptive predictor was used to exploit the spatial correlation and spectral correlation of multispectral image, finally, the secret information were embedded by a histogram shifting mechanism based on prediction error. The experimental results on multispectral images acquired by Landsat show that the proposed method has better visual quality and higher hiding capacity than the representative methods.

It has been proved that Physical Layer Network Coding (PLNC) can also improve the system throughput and spectral efficiency by taking the advantage of the broadcast nature of electromagnetic waves in wireless cooperative environments. In this paper, the basic idea of the PLNC was introduced and its benefit over traditional forward and straight-forward network coding under the two-way relay scenario was illustrated. Firstly, three types of physical layer network coding—Physical Network Coding over Finite Field (PNCF), Analog Network Coding (ANC) and Complex Field Network Coding (CFNC) were presented, the theory research development of the three kinds of PLNC were overviewed and new theory and technology related to it were introduced. Secondly, the application and implementation for the ANC scheme in the real wireless cooperative environments were overviewed. Finally, the opening issues and challenges for PLNC concerning both theory and implementation in near future were proposed．It is an important trend to improve the theory and implementation of PLNC, research the security of PLNC, and combine PLNC with other technologies, such as channel coding and modulation, relay choice, effective scheduling and resource allocation.

The estimation of the content popularity in the Content Distributed/Delivery Network (CDN) system mainly relies on the experience of administrators, which implies strong subjectivity and cannot guarantee the Quality of Service (QoS). In the paper, the authors firstly preprocessed the data, and obtained the initial knowledge base to predict the film popularity. This paper used data mining techniques to learn the existing knowledge and predict the popularity of films. Thus, the films in the CDN system could be deployed more effectively and efficiently. The movie popularity predicted by Bayesian network classier was compared with the movie popularity predicted by decision tree. On the premise of the same correct classification rate and other classification parameters, the time taken to build model in the Bayesian network classifier can be shorter. Therefore, the Bayesian network classifier was preferred. The method can solve the inaccurate deployment caused by the administrators subjectivities and improve the efficiency of the CDN system.