A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nat...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9307
Main Authors:	Ashok Kumar, Ponnai Manogaran, Arun Raj, Lakshmi Narayanan, Jyothi, B, Soliman, Naglaa F, Bajaj, Mohit, El-Shafai, Walid
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 29-11-2022 MDPI
Subjects:	A-LSTM networks Adaptation adaptive video streaming Analysis bit rate measurement client–server model Code Division Multiple Access Computer architecture Control algorithms HTTP Hypertext Internet Methods Multilayers Multimedia Neural networks Neural Networks, Computer Quality standards reference metrics Servers Signal to noise ratio Streaming media Telecommunication systems User satisfaction Video Recording - methods Video transmission Wireless communications Wireless networks Saudi Arabia A-LSTM networks bit rate measurement adaptive video streaming reference metrics video quality HTTP client–server model
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Summary:	Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s22239307