• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.53-60
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• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.

• ETRI Journal
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• v.26 no.3
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• pp.203-217
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• 2004

This paper presents a method and architecture to analyze streaming media and multimedia conferencing traffic. Our method is based on detecting the transport protocol and port numbers that are dynamically assigned during the setup between communicating parties. We then apply such information to analyze traffic generated by the most popular streaming media and multimedia conferencing applications, namely, Windows Media, Real Networks, QuickTime, SIP and H.323. We also describe a prototype implementation of a traffic monitoring and analysis system that uses our method and architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3804-3819
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• 2018

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.324-326
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• 2005

An adaptive Chunk algorithm is newly devised and a collaborative streaming is designed for high quality multimedia streaming service under time varying traffic conditions. An LMS based prediction filter is used to compensate the effect of time varying background traffic of the WAN. The underflow is generated for the $20\~28\%$ of the data stored in the central server by applying the FARIMA(Fractional Autoregressive Integrated Moving Average) traffic modeling method. The proposed algorithm is tested with the MPEG-2 video files and compensates $71\~85\%$ of central stream underflow.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.826-836
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• 2016

Increase of hypertext transfer protocol (HTTP)-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated, among others, by systems which employ adaptive bit-rate streaming. In our work, we investigate traffic originating from 120 client-server pairs, situated in an emulated laboratory environment, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative correlations, anti-persistence, and its distribution function is skewed to the right. Furthermore, we show that the traffic generated by clients employing the same or similar play-out strategies is positively correlated and synchronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3635-3654
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• 2015

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.25-33
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• 2013

P2P(Peer-to-Peer) architecture can reduce the network bandwidth and resource on the server since peers exchange data chunks with each other, while server-client architecture causes a lot of traffic on the server. Peers receive a data more reliably when the number of participating peer increases. Currently, P2P traffic has accounted for about 65% of the world's Internet traffic and diverse P2P streaming services have launched combining to video streaming technology. However, the requirements and data chunk delivery algorithms for mobile P2P streaming service should be investigated, since the existing P2P technologies have been developed and designed for the wired network. In particular, the bandwidth fluctuation caused by user mobility, wireless packet collisions, and packet losses brings about different problems on the mobile P2P streaming service compared to existing P2P streaming service. In this paper, we analyzed the problem of mobile P2P streaming services in the 802.11n wireless LAN environment through experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.837-840
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• 2003

Recently the multimedia streaming traffic such as digital audio and video in the Internet has increased tremendously. Unlike TCP, the UDP protocol, which has been used to transmit streaming traffic through the Internet, does not apply any congestion control mechanism to regulate the data flow through the shared network. If this trend is let go unchecked, these traffic will effect the performance of TCP, which is used to transport data traffic, and may lead to congestion collapse of the Internet. To avoid any adverse effort on the current Internet functionality, A study on a new protocol of modification or addition of some functionality to existing transport protocol for transmitting streaming traffic in the Internet is needed. TCP-frienly congestion control mechanism is classified with window-based congestion control scheme and rate-based congestion control scheme. In this paper, we propose an algorithm for improving the transmitting rate on a hybrid TCP-friendly congestion control scheme combined with widow-based and rate-based congestion control for multimedia streaming in the internet.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.2
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• pp.60-64
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• 2006

In typical video streaming services such as MPEG encoded video, the transmission data sizes change depending on the frame types and the required bandwidth for QoS support also changes as time passes. Accordingly, in video streaming services using the Internet it could happen that the video services stop occasionally due to instantaneous deficiencies of the required bandwidth under unexpected congestion even if the highest priority is allocated to the service by network administration. In this paper, we investigate several methods for bandwidth allocation and traffic engineering to support MPEG video traffic and propose a new method to reduce the transmission delay and enhance the throughput, there by provisioning the QoS requirements. In the proposed scheme, LSPs are established based on temporal variation patterns of the Internet traffic and the CQ_LLQ policing scheme is applied for traffic shaping at the ingress routers. Finally we verified the performance of the proposed scheme via computer simulations using OPNET.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.95-103
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• 2022

As 5G mobile communication services gradually expand in P2P (peer-to-peer) or D2D (device-to-device) applications, traffic-oriented stream control such as YouTube streaming is emerging as an important technology. In D2D communication, the type of data stream most frequently transmitted by users is a video stream, which has the characteristics of a large-capacity transport stream. In a D2D communication environment, this type of stream not only provides a cause of traffic congestion, but also degrades the quality of service between D2D User Equipments (DUEs). In this paper, we propose a Traffic-Oriented Stream Scheduling (TOSS) scheme to minimize the interruption of dynamic media streams such as video streams and to optimize streaming service quality. The proposed scheme schedules the media stream by analyzing the characteristics of the media stream and the traffic type in the bandwidth of 3.5 GHz and 28 GHz under the 5G gNB environment. We examine the performance of the proposed scheme through simulation, and the simulation results show that the proposed scheme has better performance than other comparative methods.