LATTE: Application Aware MU-MIMO Optimization

Over the last decade, the bandwidth expansion and MU-MIMO spectral efficiency have promised to increase data throughput by allowing concurrent communication between one Access Point and multiple users. MU-MIMO is a high-speed technique in IEEE 802.11ac and upcoming ax technologies that improves spectral efficiency by allowing concurrent communication between one Access Point and multiple users. In this project, we present LATTE, a novel framework that proposes Application-aware MU-MIMO optimization for multi-user multimedia applications over IEEE 802.11ac/ax. 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. Our experimental results show a scalable framework that can support a large number of users with satisfying QoE requirements.


DeepMAC: Data-Driven MAC Protocol Design Optimization

Networking protocols are designed through long-time and hard-work human efforts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual efforts to tune individual protocol parameters. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based framework to systematically design and evaluate networking protocols. We decouple a protocol into a set of parametric modules, each representing a main protocol functionality that is used as DRL input to better understand the generated protocols design optimization and analyze them in a systematic fashion. As a case study, we introduce and evaluate DeepMAC a framework in which a MAC protocol is decoupled into a set of blocks across popular flavors of 802.11 WLANs (e.g., 802.11 a/b/g/n/ac). We are interested to see what blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is able to adapt to network dynamics.


CONVINCE: Collaborative Video Analytics at the Edge

Today, video cameras are deployed in dense for monitoring physical places e.g., city, industrial, or agricultural sites. In the current systems, each camera node sends its feed to a cloud server individually. However, this approach suffers from several hurdles including higher computation cost, large bandwidth requirement for analyzing the enormous data, and privacy concerns. In dense deployment, video nodes typically demonstrate a significant spatio-temporalcorrelation. To overcome these obstacles in current approaches, this project introduces CONVINCE, a new approach to look at the network cameras as a collective entity that enables collaborative video analytics pipeline among cameras. CONVINCE aims at 1) reducing the computation cost and bandwidth requirements by leveraging spatio-temporal correlations among cameras in eliminating redundant frames intelligently, and ii) improving vision algorithms’ accuracy by enabling collaborative knowledge sharing among relevant cameras. Our results demonstrate that CONVINCE achieves an object identification accuracy of ∼91%, by transmitting only about ∼25% of all the recorded frames.


AMuSe: Online Video Rate Adaptation Using WiFi Multicast

Video delivery over wireless networks is challenging due to the lack of spectrum and reliability issues. This challenge is exacerbated in dense venues. To address this issue, Wi-Fi multicast with the ability to simultaneously multicast the same video contents to a group of users, has gained attention. For successful video delivery, the content providers are interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). The QoE ties together user perception, experience, and expectations to application and network performance. However, ensuring high QoE for multicast video streaming is challenging. Although, there have been considerable efforts in the literature to design Adaptive Bitrate (ABR) streaming algorithms to ensure the video QoE, applying these approaches to wireless multicast is not straightforward due to lack of feedback and unreliable transmissions. To overcome these issues, transmission and video rate can be jointly controlled to ensure the video QoE using Wi-Fi multicast. In this project, we have collaborated with the Adaptive Multicast Services (AMuSe) project at Lab at Columbia University which is an end to end system for high quality video delivery to a large number of users in dense environments which leverages Wi-Fi multicast.

The project involves improvements of the DYnamic Video and Rate (DYVR) algorithm which is an online control algorithm for jointly multicast transmission and video rates adaptation. We present a new channel estimation method for this algorithm. We evaluate the algorithm using the new channel estimation through extensive experiments in a push-based platform consisting of Android devices and an off-the-shelf wireless Access Point (AP). We show that our new channel estimation method improves the total performance of the algorithm significantly. We also compare two distinct versions of this algorithm against current ABR based state-of-the-art approaches.