• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.488-490
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• 2012

Nowadays QoS / QoE using technology as part of HTTP, dynamic adaptive streaming technology that became a big issue. In this paper, using HTTP, dynamic adaptive streaming technology is introduced. This dynamic adaptive streaming using HTTP-based system design and implementation. In this system, a number of MPEG2-TS file bitrate's conversion of image quality, a certain amount of time by the division of Segment, standing before the final note of the files that are used in transmission MPD (Media Presentation Description) File creation and streaming between servers and clients using HTTP to a dynamic and adaptive network environment consists of bitrate's streamers. In a network environment in which an unspecified number of seamless continuous playback of video streaming for a variety of sectors and will be utilized in the field of multimedia.

• Journal of KIISE
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• v.44 no.4
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• pp.423-432
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• 2017

Recently, the video quality control schemes for the improvement of the QoE (Quality of Experience) of video streaming services that are based on DASH (Dynamic Adaptive Streaming over HTTP), which is a standard of HTTP adaptive streaming (HAS) services, have been studied. However, the problem of the existing schemes is the degradation that is due to unnecessary quality changes because the VBR (Variable Bitrate) characteristics of the video are not considered. In this paper, we propose a SC-DASH (Segment Characteristics-based DASH) which controls the video quality based on the segment characteristics. The SC-DASH can prevent the occurrence of the unnecessary quality changes by controlling the video quality based on the size of the next segment, the segment throughput, and the buffer occupancy. The experiment results showed that the SC-DASH improves the QoE by reducing the unnecessary quality changes compared with the existing quality control schemes.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.458-463
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• 2015

These days, streaming users are using ABR (Adaptive Bitrate) technique services by requesting the most adequate video rate selectively based on their own channel states. Most ABR related video rate adaptation techniques are only focused on real-time bitrate adaptations based on their own channel state, and misses energy limited characteristics that come from a mobile device's battery dependence. In other words, the mobile device's important characteristics and accompanying energy consumption are not being considered and causes dissatisfaction over streaming services. In this paper, we propose energy efficient prefetching based dynamic adaptive video streaming techniques, which saves unnecessary consumed energy while providing video rates of the same performance. Our scheme continuously turns off energy modules with enough streaming in the buffer and turns on in case of the opposite situation to save energy. Through the performance evaluation, this study's proposed scheme is 60% better than the previous work at global average mobile download speed.

• Journal of KIISE
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• v.42 no.8
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• pp.1039-1048
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• 2015

Recently, the web-based adaptive streaming service, DASH (Dynamic Adaptive Streaming over HTTP), is receiving more attention. However, existing network-based and buffer-based video quality control schemes in DASH environments make oscillation of segment throughput, causing degradation of the quality of experience (QoE) with frequent quality changes and playback interruptions because these schemes do not consider the content characteristics. In this paper, we propose a C-DASH (Content Characteristics based Dynamic Adaptive Streaming over HTTP) scheme in order to improve the QoE in DASH environments. The C-DASH scheme performs seamless and smooth quality control based on the segment throughput, buffer status, and segment size of the content. Based on simulation results, it is confirmed that the C-DASH scheme can improve the QoE, when compared with the existing quality control schemes.

• 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 Korea Multimedia Society
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• v.19 no.7
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• pp.1166-1178
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• 2016

In Internet of Things (IoT) environments, billions of interconnected devices and multimedia sensors generate a huge amount of multimedia traffic. Since the environment are in general deployed as a server-centric architecture wireless sensor networks could be bottlenecks between IoT gateways and IoT devices. The bottleneck causes high power consumption of the device and triggers very heavy network overload by transmission of sensing data. The deterioration could decrease the quality of multimedia streaming service due to delay, loss, and waste of device power. Thus, in this paper, we propose a context-based adaptive multimedia streaming scheme to support enhanced QoS and low power consumption in IoT environments. The goal of the scheme is to increase quality score per voltage of the streaming service, given an adaptation algorithm with context that are classified network and hardware such as throughput, RTT, and CPU usage. From the both context, the quality score per voltage is used in the comparison of a only network context-based adaptive multimedia streaming scheme, a fixed multimedia streaming and our scheme. As a result, we achieves a high improvement that means the quality score per voltage is increased up to about 4, especially in case of resolution change.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.341-349
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• 2014

Dynamic adaptive streaming over HTTP (DASH) is the most recent and promising technology to support high quality streaming services. In dynamic adaptive streaming over HTTP (DASH), a client consecutively estimates the available network bandwidth and decides the transmission rate for the forthcoming video chunks to be downloaded. In this paper, we propose a novel rate adaptation algorithm called quality of experience QoE-enhanced adaptation algorithm over DASH (QAAD), which preserves the minimum buffer length to avoid interruption and minimizes the video quality changes during the playback. We implemented a DASH test bed and conducted extensive experiments. Experimental results demonstrate that under fluctuating network conditions, QAAD provides seamless streaming with stabilized video quality while the previous buffer-aware algorithm (i.e., QDASH[9]) frequently changes the video quality and undergoes the interruption.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.19-26
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• 2016

In this paper, the dynamic buffering based smart OTT platform was proposed, and analyzed for adaptive bit-rate video delivery with the optimization of HLS (HTTP Live Streaming). This platform consists of the software platform between sever and client which detects the bandwidth capacity, and adjusts the quality of the streaming for multiple bit-rates resolutions. In order to apply adaptive buffering, two buffers are added to the basic HLS player, and each buffer is responsible for constantly buffering a previous and the next channels relative to the current channel. This adaptive transmitting with smart OTT platform is superior to delivering a static video file at a single buffering, because the video stream of adaptive double buffers can be switched streaming according to client's available network speed. As a result, this proposed smart OTT can be cooperated to the application of HLS server with segmented H.265 MPEG-2 TS video & m3u8 files with its information based on the optimized transmission channel state of live and VOD, and applied to PLC transmission, too.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.308-324
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• 2018

HTTP adaptive streaming (HAS) is a promising technology for delivering video content over the Internet. HAS-based video streaming solutions rely on bandwidth estimation to select the appropriate video bitrate. Video streaming solutions that consider network conditions provide users with seamless video playback. However, when multiple clients compete for a common bottleneck link, conventional bandwidth estimation schemes that consider only one client overestimate the network bandwidth due to the ON-OFF traffic pattern. The bandwidth overestimation can cause Quality of Experience (QoE) degradation, such as unnecessary changes in video quality, and unfairness of video quality. In this paper, we propose a client-side bandwidth estimation scheme to obtain a better QoE of HAS in the multiple-client environment. The proposed scheme differentiates the client buffer status according to the buffer occupancy, and then estimates the available network bandwidth based on the buffer status and segment throughput. We evaluate the performance of HAS implemented in the ns-3 network simulator. Simulation results show that compared with the conventional schemes, the proposed scheme can enhance the QoE.