RTOP: Optimal User Grouping and SFN Clustering for Multiple eMBMS Video Sessions

Khalid, Ahmed; Zahran, Ahmed H.; Sreenan, Cormac J.

doi:10.1109/infocom.2019.8737643

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…Meanwhile, various works aimed to enhance eMBMS performances through conducting network optimizations. For example, in [5] and [6], the authors proposed to use different eNB clustering techniques in the MBSFN to improve the system performance with multiple contents in demand. In [7], the authors proposed a more efficient feedback mechanism for eMBMS UEs in order to improve their QoS performance.…”

Section: Related Workmentioning

confidence: 99%

An Effective and Efficient Dynamic eMBMS Multicast Grouping Scheduling Algorithm in MBSFNs for Public Safety Scenarios

et al. 2020

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The LTE (Long-Term Evolution) eMBMS (evolved Multimedia Broadcast Multicast Service) technology employed in MBSFNs (Multicast Broadcast Single Frequency Networks) has been shown to be capable of considerably increasing the capacity of serving public safety users under group communication scenarios. However, due to its own limitations, a more fine-tuned scheduling approach is needed in order to fully utilize the strengths of eMBMS multicast. In this work, we first identify and analyze several overlooked challenges for scheduling multicast traffic in MBSFNs. Then we develop an effective and efficient dynamic scheduling algorithm for eMBMS multicast in time and frequency varying channel. The proposed algorithm leverages the advantages of both multicast and unicast schemes via user grouping; and through extensive simulations, is shown to be capable of significantly enhancing the mission critical performances under both best effort and guaranteed bit rate delivery models. We also show the resiliency of our proposed algorithm by applying it onto various network and user deployment scenarios. Our consistent results further prove that LTE eMBMS in MBSFN is a key solution in overcoming limitations in near future public safety networks.

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Section: Related Workmentioning

confidence: 99%