Article Preview
TopSPICE protocol is an open source virtual desktop transport protocol, providing users with interactive experience similar to the local hosts. SPICE has the advantages of good image display experience, high transmission security. But it also has some shortcomings, such as large consumption of the switching image compression algorithm, lack of video processing capacity, lack of controllability (Wang, Yu, Li, Zhu, & Sheng, 2015) and so on. In order to solve these problems, some improvement approaches have been proposed. In order to achieve the progressive image transmission, Xu and Lan (2013) propose using JPEG2000 (Wang, Zhu, Liu, & Qi, 2016) compression algorithm to replace the original image compression algorithm, in which the image and video compression algorithms can be switched smoothly. Qiao (2013) proposes using discrete wavelet transform (Chen, Wang, & Cheng, 2011) to replace the original discrete cosine transform video compression in Spice to construct a new video compression method, which enhances the ability of SPICE video processing. Wang, et al (2015) adds a user controlled transparent file transfer mechanism in SPICE. Most of improvement schemes about SPICE are optimized graphics, video compression mechanism, and user controllable. However, in the video transmission, due to lack of network adaptability, SPICE consumes a lot of network bandwidth, and might causes network congestion (Kong, Zhao, & Wan, 2005) in a relatively poor network environment, especially in WAN. The service availability of cloud computing also can be reduced (Lv, Lin, Wang, Feng, & Zhou, 2015). In this article, we propose a method to improve SPICE.
Figure 1. Virtual desktop infrastructure
This paper indicates the shortcomings of SPICE on control of video traffic (Nieh, Yang, & Novik, 2003), and proposes DCVS that control video traffic output by using the virtual buffer and adjust encoder bit rate through the client feedback mechanism. DCVS enables the SPICE to dynamically adjust the image bit rate in the process of video transmission. In the end, the experiment results prove that the optimization method can effectively reduce the video bandwidth consumption and the probability of congestion by comparing the change of network traffic and the lost frame of the video before and after optimization. Figure 1 shows the virtual desktop infrastructure.