mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming

Zhou, Chao; Lin, Chia‐Wen; Guo, Zongming

doi:10.1109/tmm.2016.2522650

Cited by 126 publications

(52 citation statements)

References 20 publications

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“…Generally speaking, the decision mechanism [34]- [38] aims to maximize the average video quality, reducing the frequency of quality switching, as well as avoiding playback freezing [33].…”

Section: Dynamic Adaptive Video Streaming Over Httpmentioning

confidence: 99%

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Tillo

Xiao

et al. 2018

IEEE Trans. Multimedia

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Abstract-Multiview video streaming continues to gain popularity due to the great viewing experience it offers, as well as its availability which has been enabled by increased network throughput and other recent technical developments. User demand for interactive multiview video streaming that provides seamless view switching upon request is also increasing. However, it is a highly challenging task to stream stable and high quality videos that allow real time scene navigation within the bandwidth constraint. In this paper a convolutional neural network (ConvNet) assisted seamless multiview video streaming system is proposed to tackle the challenge. The proposed method solves the problem from two perspectives: first, a ConvNet assisted multiview representation method is proposed, which provides flexible interactivity without compromising on multiview video compression efficiency. Second, a bit allocation mechanism guided by a navigation model is developed to provide seamless navigation and adapt to network bandwidth fluctuations at the same time. These two blocks work closely to provide an optimized viewing experience to users. They can be integrated into any existing multiview video streaming framework to enhance overall performance. Experimental results demonstrate the effectiveness of the proposed method for seamless multiview streaming.Index Terms-Multiview video streaming, Multiview navigation, Multiview video representation, Convolutional neural network.

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“…Generally speaking, the decision mechanism [34]- [38] aims to maximize the average video quality, reducing the frequency of quality switching, as well as avoiding playback freezing [33].…”

Section: Dynamic Adaptive Video Streaming Over Httpmentioning

confidence: 99%

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Tillo

Xiao

et al. 2018

IEEE Trans. Multimedia

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“…mDASH presented in [13] selects an appropriate segment bit-rate by using the Markov theory [14,15]. mDASH then judges whether the selected segment bit-rate is optimal or not, by using the reward values of the following factors: (1) buffer occupancy, (2) video playback quality, (3) video rate switching frequency and amplitude, and (4) buffer overflow.…”

Section: Related Workmentioning

confidence: 99%

“…The FHBTTE values cannot predict the actual TCP throughput value properly during 0∼70 s, since it cannot detect the level shifts. On the other hand, because of the detections of the level shifts, the (8) if /V( + 1) < and ( ) > low then (9) if low−flag = then (10) low−flag = ; (11) end if (12) V( + 1) = V( ); (13) end if (14) if q(t k ) < q low and q(t k ) > q min and q low−flag = then (15) low−flag = ; (16) else if q(t k ) < q low and q(t k ) > q min and q low−flag = then HBTTE method predicts the actual TCP throughputs more accurately than the FHBTTE method does. Also, the HBTTE method shows appropriate smoothness with the detection of outliers.…”

Section: The Throughput Estimationmentioning

confidence: 99%

A Modification of the Fuzzy Logic Based DASH Adaptation Scheme for Performance Improvement

Kim

Son

Kim

2018

Wireless Communications and Mobile Computing

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We propose a modification of the fuzzy logic based DASH adaptation scheme (FDASH) for seamless media service in time-varying network conditions. The proposed scheme (mFDASH) selects a more appropriate bit-rate for the next segment by modification of the Fuzzy Logic Controller (FLC) and estimates more accurate available bandwidth than FDASH scheme by using HistoryBased TCP Throughput Estimation. Moreover, mFDASH reduces the number of video bit-rate changes by applying Segment BitRate Filtering Module (SBFM) and employs Start Mechanism for clients to provide high-quality videos in the very beginning stage of the streaming service. Lastly, Sleeping Mechanism is applied to avoid any expected buffer overflow. We then use NS-3 Network Simulator to verify the performance of mFDASH. Upon the experimental results, mFDASH shows no buffer overflow within the limited buffer size, which is not guaranteed in FDASH. Also, we confirm that mFDASH provides the highest QoE to DASH clients among the three schemes (mFDASH, FDASH, and SVAA) in Point-to-Point networks, Wi-Fi networks, and LTE networks, respectively.

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“…While the disadvantage of this approach is the increased storage requirements and the fact that adaptation is characterized by a more rough division because video bitrates can only belong to a separate set of levels. While in [14], a Markov Decision Process (MDP) is used to deal with the stochastic decision problem, which reduces both the number of playback interruptions and the number of quality level switches while increases the quality of experience. In [15], the modified adaptive algorithm is designed to implement a combination of randomness for video requests, video bitrate selection, and average harmonic based on average.…”

Section: Introductionmentioning

confidence: 99%

Improving Video Quality in DASH Systems by Proposing Adaptive Bitrate Scheme based on Variable Segment Size Approach

Jabbar¹,

Kadhim²,

Yu³

2018

IJCA

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Dynamic adaptive streaming via HTTP (DASH) has been widely disseminated over the Internet especially under the circumstances of the time varying network, which it is still the biggest challenge for providing smoothly video streaming with high quality. In DASH system, after the downloading process of a segment is completed, the player estimates the available network bandwidth by calculating the download throughput and adapting the video bitrate level based on its estimations. At client side, the DASH player uses an Adaptive Bitrate Algorithm (ABR) to choose the suitable bitrate for next segment based on current conditions. However, these adaptive algorithms discard the fact that the segment sizes greatly vary for a given video bitrate. Hence they may fail to predict the time needed for downloading the next segment. In this paper, an adaptive bitrate algorithm is proposed based on the video segment size as well as the network bandwidth estimation and the current buffer occupancy in order to accurately predict the time needed to download the next segment. Simulation results show that the proposed scheme is able to predict the download time. Also we compared the proposed scheme with other conventional schemes, we found that our proposed scheme outperforms others in achieving high video bitrates, low number of video bitrate switches, minimize the other QoE metrics such as video convergence time and the number of bitrate switching events.

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mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming

Cited by 126 publications

References 20 publications

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

A Modification of the Fuzzy Logic Based DASH Adaptation Scheme for Performance Improvement

Improving Video Quality in DASH Systems by Proposing Adaptive Bitrate Scheme based on Variable Segment Size Approach

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