AMuSe: Adaptive Multicast Services to Very Large Groups - Project Overview

Bejerano, Yigal; Gupta, Varun; Gutterman, Craig; Zussman, Gil

doi:10.1109/icccn.2016.7568491

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…In order to reach the target rate, the proposed algorithm monitors the number of devices switching from PDR of 100% to 0% to satisfy the maximum permitted number of abnormal devices. Authors in [18] designed and tested AMuSe, a mechanism to broadcast packets to a large group of STAs. AMuSe relies on the cluster formation of adjacent neighbors and feedback from cluster heads.…”

Section: Related Workmentioning

confidence: 99%

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

Gomez,

Camacho-Escoto,

Orozco-Barbosa

et al. 2023

Future Internet

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WiFi is a widely used wireless technology for data transmission. WiFi can also play a crucial role in simultaneously broadcasting content to multiple devices in multimedia transmission for venues such as classrooms, theaters, and stadiums, etc. Broadcasting allows for the efficient dissemination of information to all devices connected to the network, and it becomes crucial to ensure that the WiFi network has sufficient capacity to transmit broadcast multimedia content without interruptions or delays. However, using WiFi for broadcasting presents challenges that can impact user experience, specifically the difficulty of obtaining real-time feedback from potentially hundreds or thousands of users due to potential collisions of feedback messages. This work focuses on providing accurate feedback to the Access Point about the percentage of users not receiving broadcast traffic correctly so it can adjust its Modulation and Coding Scheme (MCS) while transmitting broadcast multimedia content to many users. The proposed method is comprised of two sequential algorithms. In order to reduce the probability of a collision after transmitting each message, an algorithm searches for the best probability value for users to transmit ACK/NACK messages, depending on whether messages are received correctly or not. This feedback allows the Access Point to estimate the number of STAs correctly/incorrectly receiving the messages being transmitted. A second algorithm uses this estimation so the Access Point can select the best MCS while maintaining the percentage of users not receiving broadcast content correctly within acceptable margins, thus providing users with the best possible content quality. We implemented the proposed method in the ns-3 simulator, and the results show it yields quick, reliable feedback to the Access Point that was then able to adjust to the best possible MCS in only a few seconds, regardless of the user density and dimensions of the scenario.

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Section: Related Workmentioning

confidence: 99%

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

Gomez,

Camacho-Escoto,

Orozco-Barbosa

et al. 2023

Future Internet

View full text Add to dashboard Cite

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“…Such a rate is not suitable for bandwidth-hungry application. Although the literature [7,44] propose adaptive WiFi multicast rate selection, they are typically designed for the previous generations of WiFi standard and do not oer the potential benets of MU-MIMO in VHT WLANs.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Pasandi

Amirpour

Nadeem

et al. 2021

Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-Assisted Video Streaming

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Over the last decade, the bandwidth expansion and MU-MIMO spectral eciency have promised to increase data throughput by allowing concurrent communication between one Access Point and multiple users. However, we are still a long way from enjoying such MU-MIMO MAC protocol improvements for bandwidth hungry applications such as video streaming in practical WiFi network settings due to heterogeneous channel conditions and devices, unreliable transmissions, and lack of useful feedback exchange among the lower and upper layers' requirements. This paper introduces MuViS, a novel dual-phase optimization framework that proposes a Quality of Experience (QoE) aware MU-MIMO optimization for multi-user video streaming over IEEE 802.11ac. MuViS rst employs reinforcement learning to optimize the MU-MIMO user group and mode selection for users based on their PHY/MAC layer characteristics. The video bitrate is then optimized based on the user's mode (Multi-User (MU) or Single-User (SU)). We present our design and its evaluation on smartphones and laptops using 802.11ac WiFi. Our experimental results in various indoor environments and congurations show a scalable framework that can support a large number of users with streaming at high video rates and satisfying QoE requirements.

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“…When the number of users grows, these solutions start to become slow and unsuitable for real time applications. Some works in the literature (e.g., [6]) tried to tackle this problem by assigning cluster heads and collecting feedback from them (instead of all users). These algorithms are relatively complicated and rely on the spacial packet loss correlation.…”

Section: Introductionmentioning

confidence: 99%

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Arefi

Khabbazian

2021

IEEE Open J. Comput. Soc.

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Packet loss recovery in wireless broadcast is challenging, particularly for real-time applications which have strict and short delivery deadline. To recover the maximum number of lost packets within a short time, existing packet recovery solutions often rely on instantly decodable network coding (IDNC). Some of these solutions can recover nearly the maximum number of lost packets possible at the cost of collecting feedback from all (or a large percentage of) users. This is impractical in dense networks. In addition, their runtime grows with the number of users, which is not desirable due to the urgent delivery deadline of real-time applications. In this work, we introduce random instantly decodable network coding (RIDNC), a random encoding approach to IDNC. We propose RAndom IDNC Encoder (RACE), a fast RIDNC encoder that can recover nearly as many lost packets as the optimal RIDNC encoder. We compare RACE with the CrowdWiFi encoder, a high performing packet loss recovery solution used in CrowdWiFi, a commercial system for broadcasting live video in dense networks. We show that RACE is up to two orders of magnitude faster than the CrowdWiFi encoder, and recovers more lost packets in practice, where there is not enough time to collect feedback from many users.

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AMuSe: Adaptive Multicast Services to Very Large Groups - Project Overview

Cited by 5 publications

References 34 publications

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

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