Interaction of AQM Schemes and Adaptive Streaming with Internet Traffic on Access Networks

We tested AQM techniques on their ability to influence end-user QoE, especially HTTP Adaptive Streaming (HAS) based video traffic on access networks. Using ns-2 we built a set of simulation scenarios for realistic Internet traffic. Based on data from recent Internet measurements, there are three major types of traffic flowing through the Internet: HTTP web traffic; HAS video traffic and progressive download (PD) video traffic. We modeled HTTP web traffic using Internet statistics published by Google. We generated dynamic HAS traffic by implementing a HAS client. We modeled PD video traffic using statistics from Youtube. We used published research to convert data from simulation traces into QoE metrics. We used realistic mixes of the three types of traffic with different loading conditions to test Random Early Detection (RED), CoDel and tail-drop queuing on DSL access networks. We also tested various options in HAS for their impact on its QoE. Most AQM configurations improved the QoE metrics of HAS and web traffic. They improved fairness among HAS streams, avoided HAS underflow in a majority of the cases where it would occur with Tail-Drop, and tangibly reduced the latency for web page downloads. Larger buffer sizes coupled with AQM has the potential to improve the QoE of all the three services at the same time. We also found that larger chunks sizes, such as 10 seconds virtually eliminated HAS underflow even in tail-drop queues.