Bandwidth Estimation and Optimized Bitrate Selection for Dynamic Adaptive Streaming Over HTTP Using RSI-GM and ISSO

Bandwidth Estimation and Optimized Bitrate Selection for Dynamic Adaptive Streaming Over HTTP Using RSI-GM and ISSO

Sanjay Agal, Priyank K. Gokani
Copyright: © 2022 |Pages: 15
DOI: 10.4018/IJCVIP.2022010107
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Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is an emerging solution that aims to standardize existing proprietary streaming systems. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. In this paper, a novel Relative Strength Index (RSI) with Geometric mean (GM) namely RSI-GM is proposed for estimating available bandwidth for DASH. The proposed work starts by taking the video as an input at the transmitter side and then the video compression is performed using the TRLE. Then MD5 hashing-based AES encryption is applied to the compressed video data to provide data security. Then RSI-GM is proposed to estimate the available bandwidth for DASH. Finally, after estimation, the bitrate for estimated bandwidth is selected optimally using the Improved Shark Smell Optimization (ISSO) algorithm.
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Introduction

The significance and usage of multimedia traffic are rapidly increasing over the last few years Hassan et al. (2020). As video traffic has grown, many commercial video providers have employed adaptive bitrates streaming techniques to provide streaming media with the best experience for users Kim et al. (2017). HTTP adaptive streaming technologies include MPEG-DASH, Apple’s HTTP Live Streaming, Microsoft’s Smooth Streaming, Adobe’s Dynamic Streaming, etc Wu et al. (2017). Due to the heterogeneity of today’s communication networks, adaptively is one of the most important requirements for any streaming systems Le et al. (2018). Accurate bandwidth estimation is an important task as it regulates the user’s buffer and influences the user-perceived Quality of Service (QoS) Mushtaq and Mellouk (2017). However, there are many challenges for providing satisfactory levels of quality-of-service (QoS) to end-users, such as bandwidth-constrained, variable capacity links, and energy-constrained operation Castellanos et al. (2019).

HTTP Adaptive Streaming (HAS) can adjust video quality to the most appropriate level on a moment-by-moment basis according to the current network condition, for example, the available network bandwidth Hwang et al. (2016). In addition, this technology can bring a chance to view videos for different users with different Internet connection capacities Phan-Xuan and Kamioka (2016). To provide the user the seamless multimedia service with maximum achievable Quality of Experience (QoE), the media content in a particular video needs to be adaptive to match the available bit rate in the network Kumar et al. (2016). This trend is expected to continue as the Internet infrastructure has evolved to support HTTP, and HTTP is firewall-friendly ur Rahman and Chung (2017).

Recently, commercial streaming services, such as Netflix and YouTube, have begun employing HAS as their default delivery method Yun et al. (2018). The 2015 Conviva report shows that almost 30% of the analyzed HAS sessions are affected by at least one freeze Petrangeli et al. (2017). Initially, Internet video streaming was implemented by using traditional streaming protocols, such as Real-Time Protocol (RTP) over User Datagram Protocol (UDP). But since firewalls usually block UDP packets in the networks, making it hard to deliver the content to the user Ayad et al. (2018).

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