## 3 Related Works  
The majority of the research works support receiver and sender-driven adaptation methods, which are carried at end devices and server-side, respectively. In the receiver-driven approach [15], the content is adapted by the receiving device just before displaying the content. [PERSON] et al. [16] proposed a multi-quality steaming method using SVC video coding. In this approach, multiple qualities of video data are streamed in a multicast communication and receivers will choose the quality of the video. [PERSON] et al. [17] developed a Resource Allocation and Layer Selection method to choose the scalable video levels in a mobility scenario. In the sender-driven method [18], receivers signal the device capabilities while creating the session; then, accordingly, the sender adapts the content and streams the adapted content. A Video Optimizer Virtual Network Function [19] has been proposed to implement dynamic video optimization, where a video processing module at kernel and Network function virtualization (NFV) are used to improve the quality of the video in 5G network. There are many work on Hypertext Transfer Protocol (HTTP)-based dynamic video adaptation methods [20; 21; 22; 23; 24] that use server-driven method. In these techniques, the server will collect the feedback on video quality, and accordingly, the video will be streamed to improve the QoE of the communication.  
There are many adaptation techniques for client-side adaptation, which mainly use Dynamic Adaptive Streaming over HTTP (DASH) and HTTP Adaptive Stream (HAS) for streaming adapted video data. [PERSON] et al. [25] proposed a Dynamic Adaptive Streaming over HTTP mechanism for wireless domain (WiDASH). Similarly, [PERSON] et al. [26] proposed a client-side adaptation technique to improve QoE of HTTP Adaptive Stream. They considered the dynamic variation of both network bandwidth and buffer capacity of the client. [PERSON] et al. [27] demonstrate the video adaptation using a feedback mechanism. Similarly, there few implementation that use client-side adaptation method and resource allocation technique [28; 29]. These implementations display the adapted content once it is fully received by the receivers. However, these techniques consider full quality while streaming from sender to receivers; hence, they consume more resources in the network. This leads us to explore more about in-network adaptation methods.  
[PERSON] et al. [30] presented a dynamic adaptation mechanism to improve QoE of the video communication. The model considers the multiple video rates for the communication. In Reference [31], a traffic engineering method has been proposed to feature the video adaptation. Here, a study has been carried to understand the importance of SDN for video streaming. A physical layer-based dynamic adaptation has been proposed in Reference [32].  
Figure 3: Architecture of Media Aware Network Elements (MANE).  
In this work, the carrier sensing-based method has been developed. [PERSON] et al. [33] proposed a streaming class-based method to stream scalable video. Here, quality levels and each level are streamed independently.  
These research works aim at in-network adaptation techniques, but they fail in handling network dynamics and video motions together. The adaptation while streaming is difficult because the adaptation module requires dynamic network conditions and video metadata to decide the adaptation parameters. The literature does not discuss the role of video metadata and network parameters, such as mobility and bandwidth availability. Additionally, adaptation requires prior knowledge of the adaptation parameters to improve the adaptation on-the-fly. The majority of the literature concentrate on adaptation techniques and lack in discussing the prior knowledge required by the adaptation engine. Hence, we are carrying experimental analysis and then derive the adaptation parameters in this research work.