Bandwidth consumption is a significant concern for online video service providers. Practical video streaming systems usually use some form of HTTP streaming (progressive download) to let users download the video at a faster rate than the video bitrate. Since users may quit before viewing the complete video, however, much of the downloaded video will be "wasted". To the extent that users' departure behavior can be predicted, we develop smart streaming that can be used to improve user QoE with limited server bandwidth or save bandwidth cost with unlimited server bandwidth. Through measurement, we extract certain user behavior properties for implementing such smart streaming, and demonstrate its advantage using prototype implementation as well as simulations.
Youtube is one of the largest video provider in the world, and attracted some academic measurement studies. @cite_15 investigated the application flow control technique utilized by YouTube. They reveal and describe the basic properties of YouTube application flow control, which is block sending. It also showed that block sending is widely used by YouTube servers. The authors also examined how the block sending algorithm interacts with the flow control provided by TCP. Paper @cite_16 studied the network characteristics of the two most popular video streaming services, Netflix and YouTube. Paper @cite_7 crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. It is reported that Youtube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span.
@cite_15 @cite_16 @cite_7
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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. YouTube is one of the most popular and largest video sharing websites (with social networking features) on the Internet. A significant percentage of videos uploaded on YouTube contains objectionable content and violates YouTube community guidelines. YouTube contains several copyright violated videos, commercial spam, hate and extremism promoting videos, vulgar and pornographic material and privacy invading content. This is primarily due to the low publication barrier and anonymity. We present an approach to identify privacy invading harassment and misdemeanor videos by mining the video metadata. We divide the problem into sub-problems: vulgar video detection, abuse and violence in public places and ragging video detection in school and colleges. We conduct a characterization study on a training dataset by downloading several videos using YouTube API and manually annotating the dataset. We define several discriminatory features for recognizing the target class objects. We employ a one class classifier approach to detect the objectionable video and frame the problem as a recognition problem. Our empirical analysis on test dataset reveals that linguistic features (presence of certain terms and people in the title and description of the main and related videos), popularity based, duration and category of videos can be used to predict the video type. We validate our hypothesis by conducting a series of experiments on evaluation dataset acquired from YouTube. Empirical results reveal that accuracy of proposed approach is more than 80 demonstrating the effectiveness of the approach. 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. This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time. In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional meta-data that should be exploited to improve the effectiveness of strategies like caching.