Estimation of Adaptation Parameters for Dynamic Video Adaptation in Wireless Network Using Experimental Method

Abstract

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.

Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application

“…Also, this algorithm achieved highest throughput of 0.85 bits. In [22], they have proposed a model called as media-aware networkelements (MANE) for improving the transmission of video in the network. This work provides a novel approach by employing an experimental assessment method to determine the optimal video qualities for a given network environment.…”

“…The threshold-based approach in the QoS improvement model [20] might not be suitable for all scenarios, and GAHC's utilization of Genetic and Hill Climbing Algorithms could be computationally expensive [21]. MANE's [22] experimental assessment method might not cover all possible network conditions, and CLONE's [23] reliance on cloned access points could introduce scalability challenges. 5G-SOS's decentralized network approach [24] might struggle to handle high traffic loads during emergencies.…”

“…𝛼(𝑟) 𝑑𝐵 = 𝒫 𝑡 − 𝒫ℒ(𝑟 0 ) − 10 𝑛 log 10 ( 𝑟 𝑟 0 ⁄ ) − 𝒳 𝜎 − 𝒫 𝑛 (12) Where, 𝒫 𝑡 represents the power essential for processing the packets, 𝒫ℒ(𝑟 0 ) represents that path-loss for a given distance which is apart from 𝑟 0 , 𝒳 𝜎 represents the zero-mean Gaussian Randomized Parameter having the standard deviation 𝜎, and 𝒫 𝑛 represents the level of noise in decibel watts. In this work, to solve the problem of multiple obstacles in the LoS, this work solves this issue by enhancing the shadowing component [22], [23] For developing an obstacle-aware RP model, in this work, we consider the adjacent MNs as the LoS obstacles. Consider MNs which would get affected because of the presence of obstacles during the transmission of data from the MNs 𝑥 to the MNs 𝑦.…”