An Adaptive Video Quality and Optimization with Enhanced Streaming Taxonomy (AVQOES)
Journal: International Journal of Multidisciplinary Research and Publications (Vol.6, No. 5)Publication Date: 2023-11-15
Authors : Koffka Khan; Wayne Goodridge;
Page : 137-147
Keywords : ;
- The Effect of Liquidity Ratio, Leverage Ratio, Profitability Ratio and Audit Committee to Financial Distress(Case Study of Mining Company Listed on the Indonesia Stock Exchange 2018-2020)
- The Influence of Liquidity Ratio, Profitability, Leverage and Market Ratio on Stock Return in Food and Beverage Sub-Sector Companies Listed on the Indonesia Stock Exchange
- The Effect of Current Ratio, Debt to Equity Ratio, Return on Equity Ratio and Price Earning Ratio on Stock Prices in the Food and Beverage Sector listed on the Indonesian Stock Exchange
- The Impact of Liquidity Ratio, Ratio of Activity, Company Size, Debt Policy, and Sales Growth on Financial Distress
- Effect of Current Ratio (CR) and Debt to Equity Ratio (DER) on Return on Assets (ROA) in Food Sub-Sector and Drinking Companies Listed in Indonesia Stock Exchange Period 2013-2016
Abstract
The AVQOES taxonomy, denoting "Adaptive Video Quality and Optimization with Enhanced Streaming," presents a comprehensive and structured framework for organizing mathematical optimization techniques applied in adaptive video streaming systems. As digital video consumption becomes increasingly ubiquitous, ensuring a seamless, high-quality viewing experience is essential. This taxonomy acknowledges the intricacies of adaptive streaming, including rate-distortion optimization, quality of experience (QoE) optimization, and throughput maximization. It covers a spectrum of optimization algorithms, adaptation variables, and constraints. Moreover, it integrates quality metrics such as PSNR, SSIM, VMAF, and both quality of experience (QoE) and quality of service (QoS) metrics, incorporating bandwidth and buffer levels. This framework discerns between client-based, in-networkbased, and server-based optimization strategies, each offering distinct advantages and constraints. Furthermore, the AVQOES taxonomy expands to include Bayesian Optimization, enriching the array of techniques available for addressing intricate, non-convex problems in the realm of adaptive video streaming. By providing this organized framework, the taxonomy empowers system designers to make informed choices, facilitating the creation of adaptive video streaming solutions that enhance the user experience, ensuring highquality content delivery in diverse network conditions and across an array of devices.
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