On the interaction between TCP-like sources and throughput-efficient scheduling policies

Giaccone, Paolo; Leonardi, E.; Neri, F.

doi:10.1016/j.peva.2012.11.003

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…control decisions in every slot and satises the virtual queues stability constraints while achieving time-average utility no smaller than 5 ⇤ . Proof 2: The proof follows from Theorem 4.5 in [11] and is omitted for brevity.…”

Section: Online Transmission and Video Rate Adaptationmentioning

confidence: 99%

“…By taking these factors into account, we rst propose a Reinforcement Learning(RL)-based MU-MIMO group and mode optimization. Then, we propose a QoE optimization of ABR video streaming over WiFi using Lyapunov optimization technique [11]. As shown in [12], the channel of a user can be signicantly dierent at a few centimeters apart in an indoor environment due to the rich multipath propagation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Online Transmission and Video Rate Adaptationmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Maximum weight matching (MWM) is a switch scheduling algorithm and has similar properties as the max-weight scheduling algorithm and backpressure. Similar to the backpressure, there is incompatibility between TCP and MWM [31], [32]. Yet, we consider backpressure routing and scheduling over wireless networks rather than switch scheduling, and we take a holistic approach to address this problem; i.e., we propose TCP-aware backpressure to make TCP and backpressure compatible.…”

Section: Related Workmentioning

confidence: 99%

TCP-aware backpressure routing and scheduling

Seferoğlu

Modiano

2014

2014 Information Theory and Applications Workshop (ITA)

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Abstract-In this work, we explore the performance of backpressure routing and scheduling for TCP flows over wireless networks. TCP and backpressure are not compatible due to a mismatch between the congestion control mechanism of TCP and the queue size based routing and scheduling of the backpressure framework. We propose a TCP-aware backpressure routing and scheduling that takes into account the behavior of TCP flows. TCP-aware backpressure (i) provides throughput optimality guarantees in the Lyapunov optimization framework, (ii) gracefully combines TCP and backpressure without making any changes to the TCP protocol, (iii) improves the throughput of TCP flows significantly, and (iv) provides fairness across competing TCP flows.

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“…By taking such factors into account, we first propose a Reinforcement Learning (RL)-based MU-MIMO group and mode optimization. We then propose a QoE optimization of ABR video streaming over WiFi using Lyapunov optimization technique [1]. A user moving at walking speed may result in a significant change in the channel.…”

Section: Introductionmentioning

confidence: 99%

Latte

Pasandi

Nadeem

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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In this paper, we present LATTE, a novel framework that proposes MU-MIMO group selection optimization for multi-user video streaming over IEEE 802.11ac. Taking a cross-layer approach, LATTE first optimizes the MU-MIMO user group selection for the users with the same characteristics in the PHY/MAC layer. It then optimizes the video bitrate for each group accordingly. We present our design and its evaluation on smartphones over 802.11ac WiFi.

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On the interaction between TCP-like sources and throughput-efficient scheduling policies

Cited by 5 publications

References 38 publications

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

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

TCP-aware backpressure routing and scheduling

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