QoE Analysis of DASH Cross-Layer Dependencies by Extensive Network Emulation

Abstract

With the rising importance of video streaming in the Internet, dynamic adaptive streaming over HTTP (DASH) has been established as a key technology for video delivery. Yet, variable network conditions often result in a limited quality of experience (QoE)—with the interrelation of cross-layer network factors and DASH mechanisms widely unexplored. To understand the complex dependencies between DASH configurations and network conditions, we propose a systematic extensive large-scale emulation approach with state-of- the-art QoE metrics. Using this approach with a real DASH player in Mininet, we emulated more than 10, 000 combinations of static and dynamic network conditions and DASH configurations to derive their QoE. The obtained results show that no single DASH configuration provides the highest achievable QoE. Depending on the network conditions, specific combinations of the TCP congestion control, segment sizes and the DASH adaptation algorithm provide higher QoE—showing the possibility of performance improvements in practice. Furthermore, the ex- tensive emulations show a linear relation between delay, loss and QoE that is mostly independent of bandwidth.