## 1 Introduction  
Video communication applications, such as video conferencing, telemedicine, video chat, and video-on-demand, are attracting users more and more in this COVID-19 pandemic situation. The communication applications involve a wireless network to reach a large number of users and enable seamless communication. Wireless networks provide the flexibility of mobility and ease of use to the users, which increases the streaming challenges and issues in providing better communication quality [1, 2]. The recent advances in wireless technology and video coding formats have opened many challenges in real-time video streaming over wireless networks. As video communication is very sensitive to jitter and throughput, it is difficult to achieve Quality of Services (QoS)and Quality of Experience (QoE) in wireless networks. The challenges of providing better quality can be handled with the help of technologies, such as Content-Aware Networking (CAN), Content-Centric Networking (CCN) [3, 4], layered video coding, and in-network video adaptation. Scalable Video Coding (SVC) [5; 6] is one such layered video coding technique that supports adaptation on-the-fly. The video encoded using SVC can be thinned as per the demand of the network and end-users to provide the best quality with the existing infrastructure. This can be realized with the help of an intelligent intermediate device, such as Media Aware Network Elements (MANE) [6], which learns the network conditions and considers the end-users requirements for extracting the partial bit-stream (thinning) to form an adapted video sequence. The MANE mainly consists of Adaptation Decision Module (ADM) and Extractor. The ADM decides the number of layers that need to be delivered to the network by considering the network, terminal, and user capabilities, availability of the network resources, and video meta-data to provide the best QoE. The adaptation parameters are used by the extractor to form an adapted video sequence from a fully scalable video bit-stream. The process of identifying suitable adaptation parameters is the major issue that needs to be addressed to improve the decision-making at ADM. This research work aimed at addressing the challenges of estimating the extraction points in wireless networking environments [7]. The work considers the influence of routing protocol and node mobility on video streaming, and the knowledge derived is used for identifying the suitable scalable video layer for adaptation. To study the impact of routing algorithm overhead on video quality, we considered node mobility in the experimentation. In this experiment, video sequences are considered based on the object's motion in the video sequences. The video sequences are categorized as high, medium, and low motion. The influences of motion levels are also used for deciding the adaptation parameters. The adaptation parameters are considered as intelligence or pre-knowledge of MANE to perform video adaptation. In this work, the video sequences are coded using Joint Scalable Video Module (JSVM) version 9.18 [8] reference software, and the wireless network environment simulated using the simulation tool OMNeT++ [9]. The key features of this research work are as follows: the experimental evaluation is carried to study the performance of the scalable video for dynamic network conditions. Second, the network dynamics, such as node mobility and node distance, are simulated to evaluate the network conditions. Third, the scalable video with different motion levels are generated and streamed over the wireless network. Finally, the adaptation parameters are estimated for dynamic network conditions and different motion levels in the video sequence. These contributions help the adaption decisions on-the-fly. The organization of the paper is as follows: Section 2 briefly explains Scalable Video Coding standards, the challenges involved in video streaming over wireless environments are discussed in Section 4, experimentation and results are discussed in Sections 5 and 6 concludes the work that is carried.