Athens University of Economics and Business


The wide deployment of multimedia services over packet networks has highlighted that bandwidthintensive and delay-sensitive applications, like video streaming, is quite challenging in mobile broadband (MBB) networks, which have stringent resource limitations and fast-changing conditions. The NESTOR project conducted a large-scale experiment based on the MONROE platform to evaluate the Quality-of-Experience (QoE) of popular video streaming services (e.g. YouTube) with active measurements in MBB environments. Special emphasis was given to adaptive video streaming (esp. MPEG-DASH) which enables the seamless adaptation of the video client to the specific network conditions of each mobile viewer and is more relevant to MBB networks. The understanding of the impact of the network parameters and the media content on the human perception are key factors in optimizing the end-to-end delivery chain.

In the first phase of the project, the objective was the design of the QoE assessment framework for MBB networks, focusing on the design of use cases, the design of the passive and active probe infrastructure, and the configuration of the MONROE nodes with all necessary scripts, tools, and thirdparty software to support the functionalities of the experiment. The MONROE Docker base container has been modified and successfully deployed, through the scheduler, in a number of testing nodes from the MONROE infrastructure in order to evaluate the video quality performance of video steaming services in real-life MBB networks.

In the second phase of the project, we conducted a large-scale experiment using the MONROE platform conducting measurements in four different countries with three mobile broadband operators for YouTube video streaming in order to benchmark the performance across operators. We collect a rich set of KPIs from the network layer and KQIs from the transport and application layer to measure the QoE of the video streaming services. In fact, the analysis showed that in the case of YouTube streaming the content server plays a major role on the delivered quality and that peering connections may significantly impact the quality. Moreover, it is shown that the quality of video streaming differs across mobile broadband operators and we quantify the differences in terms of perceived QoE. Overall, the experiment conducted in this project provides a framework for large-scale active measurements for a wide range of video streaming services and provides the tools for the analysis of the results and the quantification of the perceived QoE.

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