Reformed QoE-Based Approach in Bitrate-Adaptation for Dynamic Adaptive Streaming Systems

Nada. Abdelhafez, Mohamed S. Hassan, T. Landolsi
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引用次数: 1

Abstract

With the COVID-19 pandemic, the demand for popular streaming applications is expected to soar. Ensuring high user-perceived quality correlates with higher profits for content providers and delivery systems. Dynamic adaptive streaming over HTTP (DASH) is a widely adopted video streaming standard, utilized by service providers to provide competitive quality of experience (QoE). DASH is capable of providing seamless streaming even during uncertain network conditions by switching across different video qualities and their corresponding segment bitrates. This paper introduces a non-complex algorithm that allows smooth step-like transition in the streamed video quality. The proposed approach deploys an optimization model to manage key elements that affect the user-perceived experience such as video quality, re-buffering and quality switching events. The objective is to optimize the QoE metric, which is constrained by the network throughput, segment size and buffer occupancy to continuously select the optimum bitrate levels with low complexity.
改进的基于qos的动态自适应流系统比特率自适应方法
随着新型冠状病毒感染症(COVID-19)的扩散,对流行流媒体应用程序的需求预计将大幅增加。确保高用户感知质量与内容提供商和交付系统的高利润相关。基于HTTP的动态自适应流(DASH)是一种被广泛采用的视频流标准,服务提供商利用它来提供具有竞争力的体验质量(QoE)。即使在不确定的网络条件下,DASH也能够通过切换不同的视频质量和相应的段比特率来提供无缝的流媒体。本文介绍了一种简单的算法,可以实现流媒体视频质量的平滑阶跃转换。提出的方法部署了一个优化模型来管理影响用户感知体验的关键因素,如视频质量、重新缓冲和质量切换事件。目标是优化QoE指标,该指标受网络吞吐量、段大小和缓冲区占用的约束,以连续选择低复杂度的最佳比特率水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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