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

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.