The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Chao Zhou - One of the best experts on this subject based on the ideXlab platform.
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m dash a markov decision based Rate Adaptation approach for dynamic http streaming
IEEE Transactions on Multimedia, 2016Co-Authors: Chao Zhou, Chiawen Lin, Zongming GuoAbstract:Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any Adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based Rate Adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video Rate switching frequency and amplitude, buffer overflow/underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstRate the good performance of the proposed method in both objective and subjective visual quality.
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mdash a markov decision based Rate Adaptation approach for dynamic http streaming
IEEE Transactions on Multimedia, 2016Co-Authors: Chao ZhouAbstract:Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any Adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based Rate Adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video Rate switching frequency and amplitude, buffer overflow/underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstRate the good performance of the proposed method in both objective and subjective visual quality.
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collision detection based Rate Adaptation for video multicasting over ieee 802 11 wireless networks
International Conference on Image Processing, 2010Co-Authors: Chao Zhou, Xinggong Zhang, Zongming GuoAbstract:Wireless video multicasting/broadcasting is an efficient method for simultaneous transmission of data to a group of users. But the multicasting Rates are fixed in current IEEE 802.11 PHYs standard. In this paper, we propose a novel collision-detection based Rate-Adaptation scheme (CDRA), which fully exploits the potential of Rate Adaptation capability of wireless physical layer, to improve service qualities of video multicasting. The received signal strength indication (RSSI) and packet error ratio (PER) are comprehensively used to detect collision. The PER-guided Rate adjustment algorithm is performed when no collision happens. Otherwise the collision-avoid mechanism works. By detecting the collision, our scheme could adaptively select the maximum data Rates for video multicasting. We construct a practical multicasting test-bed in IEEE 802.11b network and carry out extensive experiments. The results show that CDRA achieves throughput gain up to 166% and PSNR gain to 139% compared with existing methods.
Soung Chang Liew - One of the best experts on this subject based on the ideXlab platform.
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a Rate Adaptation algorithm for ieee 802 11 wlans based on mac layer loss differentiation
Broadband Communications Networks and Systems, 2005Co-Authors: Qixiang Pang, Victor C M Leung, Soung Chang LiewAbstract:In a WLAN subject to variable wireless channel conditions, Rate Adaptation plays an important role to more efficiently utilize the physical link. However, the existing Rate Adaptation algorithms for IEEE 802.11 WLANs do not take into account the loss of frames due to collisions. In a WLAN with coexistence of multiple stations, two types of frame losses due to (a) link errors and (b) collisions over the wireless link can coexist and severely degrade the performance of the existing Rate Adaptation algorithms. In this paper, we propose a new automatic Rate fallback algorithm that can differentiate the two types of losses and sharpen the accuracy of the Rate Adaptation process. Numerical results show that the new algorithm can substantially improve the performance of IEEE 802.11 WLANs.
Hongkai Xiong - One of the best experts on this subject based on the ideXlab platform.
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multiuser video streaming Rate Adaptation a physical layer resource aware deep reinforcement learning approach
Visual Communications and Image Processing, 2019Co-Authors: Kexin Tang, Nuowen Kan, Junni Zou, Mingyi Hong, Hongkai XiongAbstract:In this paper, we propose a cross-layer decision framework for multiuser adaptive video delivery over time-varying and mutually interfering wireless cellular network. The key idea is to synthetically design the physical-layer optimization-based beamforming scheme (performed at the base stations) and the application-layer deep reinforcement learning (DRL)-based Rate Adaptation scheme (performed at the user terminals), so that a very complex multi-user overall fair long-term quality of experience (QoE) maximization problem can be decomposed to two layers and solved effectively. Extensive simulations show that the proposed cross-layer design is effective and promising.
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deep reinforcement learning based Rate Adaptation for adaptive 360 degree video streaming
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Kexin Tang, Chenglin Li, Hongkai XiongAbstract:In this paper, we propose a deep reinforcement learning (DRL)-based Rate Adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to reduce the possible switching latency of the field of view (FoV), we design a new QoE metric by introducing a penalty term for the large buffer occupancy. A scalable FoV method is further proposed to alleviate the combinatorial explosion of the action space in the DRL formulation. Then, we model the Rate Adaptation logic as a Markov decision process and employ the DRL-based algorithm to dynamically learn the optimal video transmission Rate. Simulation results show the superior performance of the proposed algorithm compared to the existing algorithms.
Zongming Guo - One of the best experts on this subject based on the ideXlab platform.
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m dash a markov decision based Rate Adaptation approach for dynamic http streaming
IEEE Transactions on Multimedia, 2016Co-Authors: Chao Zhou, Chiawen Lin, Zongming GuoAbstract:Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any Adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based Rate Adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video Rate switching frequency and amplitude, buffer overflow/underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstRate the good performance of the proposed method in both objective and subjective visual quality.
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collision detection based Rate Adaptation for video multicasting over ieee 802 11 wireless networks
International Conference on Image Processing, 2010Co-Authors: Chao Zhou, Xinggong Zhang, Zongming GuoAbstract:Wireless video multicasting/broadcasting is an efficient method for simultaneous transmission of data to a group of users. But the multicasting Rates are fixed in current IEEE 802.11 PHYs standard. In this paper, we propose a novel collision-detection based Rate-Adaptation scheme (CDRA), which fully exploits the potential of Rate Adaptation capability of wireless physical layer, to improve service qualities of video multicasting. The received signal strength indication (RSSI) and packet error ratio (PER) are comprehensively used to detect collision. The PER-guided Rate adjustment algorithm is performed when no collision happens. Otherwise the collision-avoid mechanism works. By detecting the collision, our scheme could adaptively select the maximum data Rates for video multicasting. We construct a practical multicasting test-bed in IEEE 802.11b network and carry out extensive experiments. The results show that CDRA achieves throughput gain up to 166% and PSNR gain to 139% compared with existing methods.
Reha M Civanlar - One of the best experts on this subject based on the ideXlab platform.
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cross layer optimized Rate Adaptation and scheduling for multiple user wireless video streaming
IEEE Journal on Selected Areas in Communications, 2007Co-Authors: Tanir Ozcelebi, Oguz M Sunay, Murat A Tekalp, Reha M CivanlarAbstract:We present a cross-layer optimized video Rate Adaptation and user scheduling scheme for multi-user wireless video streaming aiming for maximum quality of service (QoS) for each user,, maximum system video throughput, and QoS fairness among users. These objectives are jointly optimized using a multi-objective optimization (MOO) framework that aims to serve the user with the least remaining playback time, highest delivered video seconds per transmission slot and maximum video quality. Experiments with the IS-856 (1timesEV-DO) standard numerology and ITU pedestrian A and vehicular B environments show significant improvements over the state-of- the-art wireless schedulers in terms of user QoS, QoS fairness, and the system throughput.
