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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Serving multimedia content over the Internet with negligible delay remains a challenge. With the advent of Web 2.0, numerous video sharing sites using different storage and content delivery models have become popular. Yet, little is known about these models from a global perspective. Such an understanding is important for designing systems which can efficiently serve video content to users all over the world. In this paper, we analyze and compare the underlying distribution frameworks of three video sharing services - YouTube, Dailymotion and Metacafe - based on traces collected from measurements over a period of 23 days. We investigate the variation in service delay with the user's geographical location and with video characteristics such as age and popularity. We leverage multiple vantage points distributed around the globe to validate our observations. Our results represent some of the first measurements directed towards analyzing these recently popular services. This paper presents the results of an investigation into the application flow control technique utilised by YouTube. We reveal and describe the basic properties of YouTube application flow control, which we term block sending, and show that it is widely used by YouTube servers. We also examine how the block sending algorithm interacts with the flow control provided by TCP and reveal that the block sending approach was responsible for over 40 of packet loss events in YouTube flows in a residential DSL dataset and the retransmission of over 1 of all YouTube data sent after the application flow control began. We conclude by suggesting that changing YouTube block sending to be less bursty would improve the performance and reduce the bandwidth usage of YouTube video streams. Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.