DASH QoE Performance Evaluation Framework with 5G Datasets

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.