A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.

This paper describes the efficacy of rearranging and grouping of data bits. Lossless encoding techniques like Huffman Coding, Arithmetic Coding etc., works well on data which contains redundant information. The idea behind these techniques is to encode more frequently occurring symbols with less number of bits and more seldom occurring symbols with more number of bits. Most of the methods fail if there is a non-redundant data. We propose a method to re arrange and group data bits there by making the data redundant and then different lossless encoding techniques can be applied. In this paper we propose three different methods to rearrange the data bits, and efficient way of grouping them. This is first such attempt. We also justify the need of rearranging and grouping data bits for efficient lossless encoding.