Despite the growing popularity of video streaming over the Internet, problems such as re-buffering and high startup latency continue to plague users. In this paper, we present an end-to-end characterization of Yahoo's video streaming service, analyzing over 500 million video chunks downloaded over a two-week period. We gain unique visibility into the causes of performance degradation by instrumenting both the CDN server and the client player at the chunk level, while also collecting frequent snapshots of TCP variables from the server network stack. We uncover a range of performance issues, including an asynchronous disk-read timer and cache misses at the server, high latency and latency variability in the network, and buffering delays and dropped frames at the client. Looking across chunks in the same session, or destined to the same IP prefix, we see how some performance problems are relatively persistent, depending on the video's popularity, the distance between the client and server, and the client's operating system, browser, and Flash runtime.
This paper investigates HTTP streaming traffic from an ISP perspective. As streaming traffic now represents nearly half of the residential Internet traffic, understanding its characteristics is important. We focus on two major video sharing sites, YouTube and DailyMotion. We use ten packet traces from a residential ISP network, five for ADSL and five for FTTH customers, captured between 2008 and 2011. Covering a time span of four years allows us to identify changes in the service infrastructure of some providers. From the packet traces, we infer for each streaming flow the video characteristics, such as duration and encoding rate, as well as TCP flow characteristics. Using additional information from the BGP routing tables allows us to identify the originating Autonomous System (AS). With this data, we can uncover: the server side distribution policy, the impact of the serving AS on the flow characteristics and the impact of the reception quality on user behavior. A unique aspect of our work is how to measure the reception quality of the video and its impact on the viewing behavior. We see that not even half of the videos are fully downloaded. For short videos of 3 minutes or less, users stop downloading at any point, while for videos longer than 3 minutes, users either stop downloading early on or fully download the video. When the reception quality deteriorates, fewer videos are fully downloaded, and the decision to interrupt download is taken earlier. We conclude that (i) the video sharing sites have a major control over the delivery of the video and its reception quality through DNS resolution and server side streaming policy, and (ii) that only half of the videos are fully downloaded and that this fraction dramatically drops when the video reception quality is bad. User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications. The key role that video quality plays in impacting user engagement, and consequently providers' revenues, has motivated recent efforts in improving the quality of Internet video. This includes work on adaptive bitrate selection, multi-CDN optimization, and global control plane architectures. Before we embark on deploying these designs, we need to first understand the nature of video of quality problems to see if this complexity is necessary, and if simpler approaches can yield comparable benefits. To this end, this paper is a first attempt to shed some light on the structure of video quality problems. Using measurements from 300 million video sessions over a two-week period, we identify recurrent problems using a hierarchical clustering approach over the space of client session attributes (e.g., CDN, AS, connectivity). Our key findings are that: (1) a small number (2 ) of critical clusters account for 83 of join failure problems (44--84 for other metrics); (2) many problem events (50 ) persist for at least 2 hours; (3) a majority of these problems (e.g., 60 of join failures, 30--60 for other metrics) are related to content provider, CDN, or client ISP issues. Building on these insights, we evaluate the potential improvement by focusing on addressing these recurrent problems and find that fixing just 1 of these clusters can reduce the number of problematic sessions by 55 for join failures (15 --40 for other metrics). The 2008 Beijing Olympics was an interesting event from a VoD perspective because it involved near real-time video delivery at massive scales over multiple days of a high-profile event. We present some measurement-driven insights into this event through a unique dataset obtained from ChinaCache, the largest CDN in China. The dataset is unique in three respects. First, it gives a "white-box" view into user access patterns which would otherwise be impossible. Second, since the CDN serves different content providers, it allows to compare and contrast the effects of different presentation models on end users. Third, the nature of the content itself is vastly different from traditional VoD systems in terms of the real-time and event-driven nature, which gives rise to unique effects. The dataset allows us to investigate a wide range of interesting issues: (1) how the live nature of the events causes differences in access patterns compared to traditional VoD and User-Generated Content (UGC) systems, (2) how the presentation models affect user behavior, and (3) flash-crowd phenomena. Based on these observations, we discuss implications for future live VoD systems.