The cost of aggressive HTTP adaptive streaming: Quantifying YouTube´s redundant traffic

Video content and, in particular, YouTube´s content account for the largest amount of today´s Internet traffic. However, little is known about the behavior of video streaming services for different kinds of network environments and under varying network conditions. Due to network operators´ lack of knowledge about the transmitted content, network resources may not be optimally used in general. Thus, we propose a dyadic measurement system composed of application, i.e., client-based and network-based monitoring for YouTube´s video traffic. Using our proposed monitoring methodology, we analyze the behavior of YouTube´s HTTP-based adaptive video streaming mechanisms. In detail, we quantify via experimental measurements on real network traffic YouTube´s behavior for different videos under static and varying network conditions. Our measurement results show that in case of varying network conditions, YouTube demands different video qualities in parallel in order to adapt to the network situation. However, this behavior can result in up to 33 % of redundant network traffic, i.e., downloaded video content of different quality levels for the same play time. Due to our findings, network operators should try to optimize the allocation of network resources for video content in a way that avoids varying network conditions, resulting in less waste of network resources.