• ETRI Journal
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• v.35 no.1
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• pp.27-34
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• 2013

This paper proposes a network-adaptive mechanism for HTTP-based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time-variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.374-395
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• 2017

Users quite often experience volatile channel conditions which negatively influence multimedia transmission. HTTP adaptive streaming has emerged as a new promising technology where the video quality can be adjusted to variable network conditions. Nevertheless, the new technology does not remain without drawbacks. As it has been observed, multiple video players sharing the same network link have often problems with achieving good efficiency and stability of play-out due to a mutual interference and competition among video players. Our investigation indicates that there may be another cause for under-performance of the streamed video. In an emulated environment, we implemented three algorithms of adaptive video play-out based on bandwidth or buffer assessment. As we show, traffic generated by players 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. However, when some of the parameters of the play-out strategies are randomised, the correlation and synchronisation diminish what has a positive impact on the smoothness of the traffic and on the video quality perceived by end users. Our research shows that non-correlated traffic flows generated by play-out strategies improve efficiency and stability of streamed adaptive video.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.43-48
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• 1997

The video transmission for real-time viewing over the Internet is a core operation for the multimedia services. However, its realization is very difficult because the Internet has two major problems, namely, very narrow endpoint-bandwidth and the network jitter. We already proposed a scalable video transmission method in [8] which used MPEG-4 video VM(Verification Model) 2.0[3] for very low bit rate coding and an adaptive temporal rate control of video objects to overcome the network jitter problem. In this paper, we present the improved adaptive temporal rate control scheme for the scalable transmission. Experimental results for three test video sequences show that the adaptive temporal rate control can transfer the video bitstream at source frame rate under variable network condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3200-3219
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• 2013

This paper proposes an adaptive combined scalable video coding (CSVC) system for video transmission over MIMO-OFDM (Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing) broadband wireless communication systems. The scalable combination method of CSVC adaptively combines the medium grain scalable (MGS), the coarse grain scalable (CGS) and the scalable spatial modes with the limited feedback partially from channel state information (CSI) of MIMO-OFDM systems. The objective is to improve the average of peak signal-to-noise ratio (PSNR) and bit error rate (BER) of the received video stream by exploiting partial CSI of video sources and channel condition. Experimental results show that the delivered quality using the proposed adaptive CSVC over MIMO-OFDM system performs better than those proposed previously in the literature.

• 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.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1182-1194
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• 2018

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

• Journal of KIISE
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• v.45 no.2
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• pp.187-194
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• 2018

Recently, Hypertext Transfer Protocol (HTTP) adaptive streaming service is attracting attention. The existing quality adaptive scheme of HTTP adaptive streaming service adjusts the video quality according to the network bandwidth or the client buffer size. However, the problem with the existing quality adaptive scheme is the QoE (Quality of Experience) degradation caused by the unnecessary quality change that occurs due to frequent bandwidth change or fixed buffer threshold. We propose a video quality maintenance scheme that improves average video quality and minimizes unnecessary quality change in order to improve the QoE of HTTP adaptive streaming service in the changing network environment. The proposed scheme maintains high quality for a long time by setting the quality maintenance duration to be long when buffer occupancy and video quality are high. The experimental results show that the proposed scheme improves QoE by improving the average video quality and minimizing the quality change.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4585-4603
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• 2015

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4068-4086
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• 2014

Due to the prevalence of powerful mobile terminals and the rapid advancements in wireless communication technologies, the wireless video streaming service has become increasingly more popular. Recent studies show that video streaming services via Transmission Control Protocol (TCP) are becoming more practical. TCP has more advantages than User Diagram Protocol (UDP), including firewall traversal, bandwidth fairness, and reliability. However, each video service shares an equal portion of the limited bandwidth because of the fair sharing characteristics inherent in TCP and this bandwidth fair sharing cannot always guarantee the video quality for each user. To solve this challenging problem, an Adaptive Multiple TCP (AM-TCP) scheme is proposed in this paper to guarantee the video quality for mobile devices in wireless networks. AM-TCP adaptively controls the number of TCP connections according to the video Rate Distortion (RD) characteristics of each stream and network status. The proposed scheme can minimize the total distortion of all participating video streams and maximize the service quality by guaranteeing the quality of each video streaming session. The simulation results show that the proposed scheme can significantly improve the quality of video streaming in wireless networks.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.104-114
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• 2018

Recently, as HTTP-based video streaming traffic continues to increase, Dynamic Adaptive Streaming over HTTP(DASH), which is one of the HTTP-based adaptive streaming(HAS) technologies, is receiving attention. Accordingly, many video quality control techniques have been proposed to provide a high quality of experience(QoE) to clients in a DASH environment. In this paper, we propose a new quality control method using ANFIS(Adaptive Network based Fuzzy Inference System) which is one of the neuro-fuzzy system structure. By using ANFIS, the proposed scheme can find fuzzy parameters that selects the appropriate segment bitrate for clients. Also, considering the characteristic of VBR video, the next segment download time can be more accurately predicted using the actual size of the segment. And, by using this, it adjusts video quality appropriately in the time-varying network. In the simulation using NS-3, we show that the proposed scheme shows higher average segment bitrate and lower number of bitrate-switching than the existing methods and provides improved QoE to the clients.