Massive video analytics systems, comprised of many densely deployed cameras and supporting edge servers, are driving innovation in many areas including smart retail stores and security monitoring. To support such systems the challenge lies in collecting video footage in a way that maximizes end-to-end application goals, and scales this performance as camera density increases to meet application needs. This paper presents Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design that meets these needs. To mitigate physical mmWave link blockage, Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that works hand-in-hand toward the application-level objective of maximizing video analytics accuracy, rather than simply maximizing data throughput. Our experimental evaluation uses a combination of testbed deployment and trace-driven simulation and compares against both Wi-Fi and mmWave mesh schemes that operate without Spider’s algorithms. Results show that Spider is able to sup-port camera densities up to 176% higher (gains of 2.76×) than the best-performing comparison scheme, allowing it alone to meet real-world camera density targets (4–250 cameras/1,000 sq. ft., depending on application). Further experiments demonstrate Spider’s scalability in the presence of failures, with a 5.4–100× reduction in average failure recovery time.

Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that achieves 77-99% of an oracle system's QoE and outperforms TikTok by 43.9-45.1x, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.

LoRaWAN has emerged as an appealing technology to connect IoT devices but it functions without explicit coordination among transmitters, which can lead to many packet collisions as the network scales. State-of-the-art work proposes various approaches to deal with these collisions, but most functions only in high signal-to-interference ratio (SIR) conditions and thus does not scale to real scenarios where weak receptions are easily buried by stronger receptions from nearby transmitters. In this paper, we take a fresh look at LoRa’s physical layer, revealing that its underlying linear chirp modulation fundamentally limits the capacity and scalability of concurrentLoRa transmissions. We show that by replacing linear chirps with their non-linear counterparts, we can boost the throughput of concurrent LoRa transmissions and empower the LoRa receiver to successfully receive weak transmissions in the presence of strong colliding signals. Such a non-linear chirp design further enables the receiver to demodulate fully aligned collision symbols — a case where none of the existing approaches can deal with. We implement these ideas in a holistic LoRaWAN stack based on the USRP N210 software-defined radio platform. Our head-to-head comparison with two state-of-the-art research systems and a standard LoRaWAN base-line demonstrates that CurvingLoRa improves the network throughput by 1.6–7.6× while simultaneously sacrificing neither power efficiency nor noise resilience.