Browsing by Author "Fan, Ching-Ling"

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Optimizing Fixation Prediction Using Recurrent Neural Networks for 360 degrees Video Streaming in Head-Mounted Virtual Reality
(2020-03-01)
Fan, Ching-Ling
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Yen, Shou-Cheng
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Huang, Chun-Ying
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Hsu, Cheng-Hsin
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資訊工程學系
We study the problem of predicting the viewing probability of different parts of 360. videos when streaming them to head-mounted displays. We propose a fixation prediction network based on recurrent neural network, which leverages sensor and content features. The content features are derived by computer vision (CV) algorithms, which may suffer from inferior performance due to various types of distortion caused by diverse 360. video projection models. We propose a unified approach with overlapping virtual viewports to eliminate such negative effects, andwe evaluate our proposed solution using severalCValgorithms, such as saliency detection, face detection, and object detection. We find that overlapping virtual viewports increase the performance of these existing CV algorithms that were not trained for 360. videos. We next fine-tune our fixation prediction network with diverse design options, including: 1) with or without overlapping virtual viewports, 2) with or without future content features, and 3) different feature sampling rates. We empirically choose the best fixation prediction network and use it in a 360. video streaming system. We conduct extensive trace-driven simulations with a large-scale dataset to quantify the performance of the 360. video streaming system with different fixation prediction algorithms. The results show that our proposed fixation prediction network outperforms other algorithms in several aspects, such as: 1) achieving comparable video quality (average gaps between -0.05 and 0.92 dB), 2) consuming much less bandwidth (average bandwidth reduction by up to 8Mb/s), 3) reducing the rebuffering time (on average 40 s in bandwidth-limited 4G cellular networks), and 4) running in real-time (at most 124 ms).
Smart Beholder: An Extensible Smart Lens Platform
(2016)
Huang, Chun-Ying
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Fan, Ching-Ling
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Hsu, Chih-Fan
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Chang, Hsin-Yu
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Tsai, Tsung-Han
Smart Lenses refer to detachable, orientable and zoomable lenses that stream live videos over wireless networks to heterogeneous computing devices, including tablets and smartphones. Various novel applications are made possible by smart lenses, including mobile photography, smart surveillance cameras, and Unmanned Aerial Vehicle (UAV) cameras. However, to our best knowledge, existing smart lenses are closed and proprietary, and thus we initiate an open-source project called Smart Beholder for end-to-end solutions of smart lenses. The code and documents of Smart Beholder can be found at our website http://www.smartbeholder.org. Our Smart Beholder platform are useful to researchers for fast prototyping, developers for rapid development, and amateurs for hobbies. We have implemented Smart Beholder server (camera) using a popular embedded Linux platform, called Raspberry Pi. We have also realized Smart Beholder client (controller) on various OS\'s, including Android. Our experimental results show the practicality and efficiency of our proposed Smart Beholder: we outperform commercial products in the market in terms of both objective and subjective metrics. We believe the release of Smart Beholder will stimulate future studies on novel multimedia applications enabled by smart lenses.
Streaming Scalable Video Sequences with Media-Aware Network Elements Implemented in P4 Programming Language
(2018-01-01)
Wang, Guan-Ru
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Chen, Chien
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Chen, Chao-Wen
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Pan, Li -Wen
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Wang, Yu-Kong
We present the first Media-Aware Network Element (MANE) for intelligently streaming scalable video sequences in P4 programming language. Our MANE selectively drops queued scalable video packets when the queue occupancy exceeds a threshold. Three packet discarding logics are implemented: (i) tail, (ii) enhancement-layer, and (iii) rate-distortion optimized. Our P4-based MANE implementation is demonstrated in: (i) larger emulated networks in mininet with P4 software switches and (ii) a small real network with a physical P4 switch and multiple Raspberry Pis running P4 software switches.
Toward an Adaptive Screencast Platform: Measurement and Optimization
(2016-12-01)
Hsu, Chih-Fan
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Fan, Ching-Ling
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Tsai, Tsung-Han
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Huang, Chun-Ying
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Hsu, Cheng-Hsin
The binding between computing devices and displays is becoming dynamic and adaptive, and screencast technologies enable such binding over wireless networks. In this article, we design and conduct the first detailed measurement study on the performance of the state-of-the-art screencast technologies. Several commercial and one open-source screencast technologies are considered in our detailed analysis, which leads to several insights: (1) there is no single winning screencast technology, indicating room to further enhance the screencast technologies; (2) hardware video encoders significantly reduce the CPU usage at the expense of slightly higher GPU usage and end-to-end delay, and should be adopted in future screencast technologies; (3) comprehensive error resilience tools are needed as wireless communication is vulnerable to packet loss; (4) emerging video codecs designed for screen contents lead to a better Quality of Experience (QoE) of screencast; and (5) rate adaptation mechanisms are critical to avoiding degraded QoE due to network dynamics. As a case study, we propose a nonintrusive yet accurate available bandwidth estimation mechanism. Real experiments demonstrate the practicality and efficiency of our proposed solution. Our measurement methodology, open-source screencast platform, and case study allow researchers and developers to quantitatively evaluate other design considerations, which will lead to optimized screencast technologies.