67 research outputs found
MSPlayer: Multi-Source and multi-Path LeverAged YoutubER
Online video streaming through mobile devices has become extremely popular
nowadays. YouTube, for example, reported that the percentage of its traffic
streaming to mobile devices has soared from 6% to more than 40% over the past
two years. Moreover, people are constantly seeking to stream high quality video
for better experience while often suffering from limited bandwidth. Thanks to
the rapid deployment of content delivery networks (CDNs), popular videos are
now replicated at different sites, and users can stream videos from close-by
locations with low latencies. As mobile devices nowadays are equipped with
multiple wireless interfaces (e.g., WiFi and 3G/4G), aggregating bandwidth for
high definition video streaming has become possible.
We propose a client-based video streaming solution, MSPlayer, that takes
advantage of multiple video sources as well as multiple network paths through
different interfaces. MSPlayer reduces start-up latency and provides high
quality video streaming and robust data transport in mobile scenarios. We
experimentally demonstrate our solution on a testbed and through the YouTube
video service.Comment: accepted to ACM CoNEXT'1
Traffic Profiling for Mobile Video Streaming
This paper describes a novel system that provides key parameters of HTTP
Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A
non-intrusive traffic profiling solution is proposed that observes packet flows
at the transmit queue of base stations, edge-routers, or gateways. By analyzing
IP flows in real time, the presented scheme identifies different phases of an
HAS session and estimates important application-layer parameters, such as
play-back buffer state and video encoding rate. The introduced estimators only
use IP-layer information, do not require standardization and work even with
traffic that is encrypted via Transport Layer Security (TLS). Experimental
results for a popular video streaming service clearly verify the high accuracy
of the proposed solution. Traffic profiling, thus, provides a valuable
alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order
to perform efficient network optimization for video streaming.Comment: 7 pages, 11 figures. Accepted for publication in the proceedings of
IEEE ICC'1
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Mobile Video Is Inefficient: A Traffic Analysis
Video streaming on mobile devices is on the rise. According to recent reports, mobile video streaming traffic accounted for 52.8% of total mobile data traffic in 2011, and it is forecast to reach 66.4% in 2015. We analyzed the network traffic behaviors of the two most popular HTTP-based video streaming services: YouTube and Netflix. Our research indicates that the network traffic behavior depends on factors such as the type of device, multimedia applications in use and network conditions. Furthermore, we found that a large part of the downloaded video content can be unaccepted by a video player even though it is successfully delivered to a client. This unwanted behavior often occurs when the video player changes the resolution in a fluctuating network condition and the playout buffer is full while downloading a video. Some of the measurements show that the discarded data may exceed 35% of the total video content
Deep learning approach for real-time video streaming traffic classification
Video streaming services such as Amazon Prime
Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples’ lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Video streaming services such as Amazon Prime
Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples’ lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification.
Based on the performance evaluation, the model produces an
overall accuracy of 99.3% when classifying video streaming
traffic using a multi-layer feedforward neural network. This
paper also evaluates the DL approach’s effectiveness compared
to the Gaussian Naive Bayes algorithm (GNB), one of the most
well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4% overall accuracy
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