Video streaming represents a large fraction of Internet traffic. Surprisingly, little is known about the network characteristics of this traffic. In this paper, we study the network characteristics of the two most popular video streaming services, Netflix and YouTube. We show that the streaming strategies vary with the type of the application (Web browser or native mobile application), and the type of container (Silverlight, Flash, or HTML5) used for video streaming. In particular, we identify three different streaming strategies that produce traffic patterns from non-ack clocked ON-OFF cycles to bulk TCP transfer. We then present an analytical model to study the potential impact of these streaming strategies on the aggregate traffic and make recommendations accordingly.
Plissonneau al @cite_13 , Saxena al @cite_15 , and Alcock al @cite_20 observe rate limitations on YouTube traffic. They do not identify the streaming strategies discussed in our paper. Akhshabi al @cite_2 only observed a rate limitation in the steady state phase for Netflix. Saxena al @cite_15 show that the YouTube videos streamed using the servers of Google have a buffering phase, whereas the legacy servers of YouTube do not show this buffering phase. Alcock @cite_20 only characterized the strategy for short ON-OFF cycles for Flash videos on YouTube.
@cite_15 @cite_13 @cite_20 @cite_2
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YouTube traffic is bursty. These bursts trigger packet losses and stress router queues, causing TCP's congestion-control algorithm to kick in. In this paper, we introduce Trickle, a server-side mechanism that uses TCP to rate limit YouTube video streaming. Trickle paces the video stream by placing an upper bound on TCP's congestion window as a function of the streaming rate and the round-trip time. We evaluated Trickle on YouTube production data centers in Europe and India and analyzed its impact on losses, bandwidth, RTT, and video buffer under-run events. The results show that Trickle reduces the average TCP loss rate by up to 43 and the average RTT by up to 28 while maintaining the streaming rate requested by the application. Video streaming represents a large fraction of Internet traffic. Surprisingly, little is known about the network characteristics of this traffic. In this paper, we study the network characteristics of the two most popular video streaming services, Netflix and YouTube. We show that the streaming strategies vary with the type of the application (Web browser or native mobile application), and the type of container (Silverlight, Flash, or HTML5) used for video streaming. In particular, we identify three different streaming strategies that produce traffic patterns from non-ack clocked ON-OFF cycles to bulk TCP transfer. We then present an analytical model to study the potential impact of these streaming strategies on the aggregate traffic and make recommendations accordingly. In this paper, we analyze the YouTube service and the traffic generated from its usage. The purpose of this study is to identify by strictly using passive measurements the information that can be used as metrics or indicators of the progress of individual video sessions and to estimate the impact of these metrics in the user experience. We find a novel method to track the progress of the video playback that, in contrast to previous works, does not require instrumentation of the video player neither browser-based plug-ins. Instead, we extract important statistical information about the status of the playback by reverse engineering the metrics in related HTTP requests that are generated during playback. For the purpose of collecting these metrics, a tool was developed to perform YouTube traffic measurements by means of passive network monitoring in a large university campus network. The analysis of the obtained data revealed the most important sources of initial delay in the sessions as well as buffer outage events and download rate statistics. Further analysis revealed the impact of video advertisements and re-buffering events on the user experience in terms of video abandonment rate. Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20 compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.