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

Ponnai Manogaran Ashok Kumar; Lakshmi Narayanan Arun Raj; B Jyothi; Naglaa F Soliman; Mohit Bajaj; Walid El-Shafai

doi:10.3390/s22239307

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

Sensors (Basel). 2022 Nov 29;22(23):9307. doi: 10.3390/s22239307.

Authors

Ponnai Manogaran Ashok Kumar¹, Lakshmi Narayanan Arun Raj², B Jyothi³, Naglaa F Soliman⁴, Mohit Bajaj⁵, Walid El-Shafai^{6

7}

Affiliations

¹ Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522 302, India.
² Department of Computing Science and Engineering, B.S.A. Crescent Institute of Science and Technology, Vandalur, Chennai 600 048, India.
³ Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram 522 302, India.
⁴ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁵ Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun 248 002, India.
⁶ Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia.
⁷ Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt.

Abstract

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.

Keywords: A-LSTM networks; HTTP; adaptive video streaming; bit rate measurement; client–server model; reference metrics; video quality.

MeSH terms

Neural Networks, Computer*
Video Recording / methods

Grants and funding

This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.