QoE-Aware Video Multicast Mechanism in Fiber-Wireless Access Networks

Li, Zhidu; Wang, Qingqing; Zou, Hong

doi:10.1109/access.2019.2938422

Cited by 10 publications

(2 citation statements)

References 27 publications

(32 reference statements)

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“…Zhang et al proposed a multicast scheme with NOMA and scalable video coding (SVC) to improve the overall Quality of Experience (QoE) [32]. Similarly, aiming to improve QoE, Li et al formulated the multicast adaptation problem as a minimum dominating set problem and developed a mechanism with QoE guarantee [33]. In addition to video quality, Guo et al also took power consumption into account and solved the optimal transmission time, power allocation, and encoding rate to maximize video quality and minimize power consumption [34].…”

Section: Related Workmentioning

confidence: 99%

Priority-Aware Resource Allocation for 5G mmWave Multicast Broadcast Services

Lin

et al. 2023

IEEE Trans. on Broadcast.

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5G Multicast Broadcast Services (MBS) are viewed as a promising 5G New Radio (NR) application, as standardization begins in 3GPP Release 17. With MBS, one next generation Node B (gNB) delivers data to multiple user equipments (UE) simultaneously, thus improving spectrum efficiency. Millimeter wave (mmWave) beamforming further enhances system performance by focusing signals in a dedicated direction. However, despite the advantages, we identify three issues of multicast with beamforming techniques. First, link directionality causes the gNB to transmit data over the beams sequentially, resulting in a combinatorial resource allocation problem. Confronting this beam scheduling issue, we develop an optimization algorithm that obtains an optimal solution in polynomial time. Second, UEs may falsely report their valuations over beam resources to gain more utility. Under this scenario, the gNB cannot allocate the resources to those in need due to the lack of accurate UE information. Therefore, we propose a Vickrey-Clarke-Groves (VCG) auction-based mechanism to incentivize the UEs to reveal their valuations over resources truthfully. This mechanism guarantees solution efficiency and maximizes social welfare. Third, as 3GPP standards allow for different priorities for different multicast flows, and video content providers distinguish between ordinary and premium UEs, we take UE priority into account. In this regard, we extend the valuation-based mechanism to a multipriority one. Finally, the mathematical analysis validates some desirable properties of the proposed scheme, such as incentivecompatibility. Simulation results also justify our superior performance in the 5G MBS system compared with other resource allocation schemes.

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Section: Related Workmentioning

confidence: 99%

Priority-Aware Resource Allocation for 5G mmWave Multicast Broadcast Services

Lin

et al. 2023

IEEE Trans. on Broadcast.

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“…The current immersive video transmission research on the wireless networks is mainly based on the MPEG-DASH technology [28]- [32], in which the quality of the video chunk to be downloaded is determined by observing the throughput of the network and the length of the client buffer. These adaptive bitrate (ABR) algorithms run on the client video player, dynamically selecting the video bitrate for each video chunk to maximize the user's QoE.…”

Section: Introductionmentioning

confidence: 99%

Network-Assisted Neural Adaptive Naked-Eye 3D Video Streaming Over Wireless Networks

Liu

Wang

et al. 2019

IEEE Access

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High quality transmission of Virtual Reality (VR) video depends strictly on bandwidth and delay requirements. It becomes possible with the maturity and commercialization of 5G technology. However, to transmit VR video streaming over wireless communication system, we must cope with the challenge that fluctuation exists in the wireless channel conditions. For example, available channel bandwidth, packet loss rate, and interference level may vary with time due to channel fading and user's mobility. The problem is more prominent when transmitting naked-eye 3D video which generally consists of multiple viewpoints with different resolutions. In order to optimize the quality of experience (QoE) of watching naked-eye VR video over wireless networks, this paper proposes a network-assisted neural adaptive video streaming algorithm (NAVSA). Specifically, we present a modified QoE function to describe the quality of naked-eye 3D streaming quantitatively, which not only considers the quality of naked-eye 3D video itself, but also considers the phenomenon of rebuffering and video fluctuation that occurs during video transmission. Next, with the network-assisted feedback, the physical layer information, the buffer occupancy of the video client, and the size of the next video chunk are collected to train a reinforcement learning model. Based on dynamic adaptive streaming over HTTP (DASH), the model can automatically choose appropriate viewpoints and resolutions corresponding to the current condition of the wireless networks such that the network capacity can be fully explored. To verify the performance of our proposed NAVSA, we simulate 3 naked-eye 3D video application scenarios on the NS3 platform. The results show that the performance of NAVSA is about 5∼8% better than some state-of-the-art algorithms in wireless networks.

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