The popularity of 4K videos is on the rise. However, streaming such high-quality videos over mm Wave to several users presents significant challenges due to directional communication, fluctuating channels, and high bandwidth demands. To address these challenges, this paper introduces an innovative 4K layered video multicast streaming system. We (i) develop a video quality model tailored for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) design a streaming strategy that integrates fountain code to eliminate redundancy in multicast groups, coupled with a Leaky-Bucket approach for congestion control. We implement our system on Commodity-Off- The-Shelf (COTS) WiGig devices and demonstrate its effectiveness through comprehensive testbed and emulation experiments.

Extreme mobility becomes a norm rather than an exception with emergent high-speed rails, drones, industrial IoT, and many more. However, 4G/5G mobility management is not always reliable in extreme mobility, with non-negligible failures and policy conflicts. The root cause is that, existing mobility management is primarily based on wireless signal strength. While reasonable in static and low mobility, it is vulnerable to dramatic wireless dynamics from extreme mobility in triggering, decision, and execution. We deviseREM, Reliable Extreme Mobility management for beyond 5G cellular networks while maintaining backward compatibility to 4G/5G.REMshifts to movement-based mobility management in the delay-Doppler domain. Its signaling overlay relaxes feedback via cross-band estimation, simplifies policies with provable conflict freedom, and stabilizes signaling via scheduling-based OTFS modulation. Our evaluation with operational high-speed rail datasets shows that,REMreduces failures comparable to static and low mobility, with low signaling and latency cost.REMreduces the network failures by up to an order of magnitude, eliminates policy conflicts, and improves application performance by 31.8% - 88.3% compared to legacy 4G/5G.

The wireless signal propagates via multipath arising from different reflections and penetration between a transmitter and receiver. Extracting multipath profiles (e.g., delay and Doppler along each path) from received signals enables many important applications, such as channel prediction and crossband channel estimation (i.e., estimating the channel on a different frequency). The benefit of multipath estimation further increases with mobility since the channel in that case is less stable and more important to track. Yet high-speed mobility poses significant challenges to multipath estimation. In this paper, instead of using time-frequency domain channel representation, we leverage the delay-Doppler domain representation to accurately extract and predict multipath properties. Specifically, we use impulses in the delay-Doppler domain as pilots to estimate the multipath parameters and apply the multipath information to predicting wireless channels as an example application. Our design rationale is that mobility is more predictable than the wireless channel since mobility has inertial while the wireless channel is the outcome of a complicated interaction between mobility, multipath, and noise. We evaluate our approach via both acoustic and RF experiments, including vehicular experiments using USRP. Our results show that the estimated multipath matches the ground truth, and the resulting channel prediction is more accurate than the traditional channel prediction schemes.

Real-time mobile video analytics plays an increasingly important role in our daily life, such as smart driving, unmanned delivery, cashier free stores, and video surveillance. The existing video analytics runs complex deep models to detect and recognize objects in video frames. However, running deep models on mobile devices can not meet the real-time requirement. This paper develops a novel mobile video analytics system. Its unique features include (i) high accuracy, (ii) real-time, and (iii) running exclusively on a mobile device without the need of edge/cloud server or network connectivity. At its heart lies an effective technique to reliably extract motion from video frames and use the motion to speed up video analytics. Unlike the existing motion extraction, our technique is robust to background noise and changes in object sizes. Extensive evaluation results show that we can support real-time object tracking at 30 frames/second (fps) on Nvidia Jetson TX2. For single-object tracking, Sight improves the average Intersection-over-Union (IoU) by 88%, improves the mean Average Precision (mAP) by 207% and reduces the average hardware resource usage by 45% over state-of-the-art approach. For multi-object tracking, Sight improves IoU by 69%, improves mAP by 173% and reduces resource usage by around 32% over state-of-the-art approach.

Performance impacts are commonly observed in cellular networks and are induced by several factors, such as software upgrade and configuration changes. The variability in traffic patterns across different granularities can lead to impact cancellation or dilution. As a result, performance impacts are hard to capture if not aggregated over problematic features. Analyzing performance impact across all possible feature combinations is too expensive. On the other hand, the set of features that causes issues is unpredictable due to the highly dynamic and heterogeneous cellular networks. In this paper, we propose a novel algorithm that dynamically explores those network feature combinations that are likely to have problems by using a summary structure Sketch. We further design a neural network based algorithm to localize root cause. We achieve high scalability in neural network by leveraging the Lattice and Sketch structure. We demonstrate the effectiveness of our impact detection and diagnosis through extensive evaluation using data collected from a major tier-1 cellular carrier in US and synthetic traces.

Extreme mobility has become a norm rather than an exception. However, 4G/5G mobility management is not always reliable in extreme mobility, with non-negligible failures and policy conflicts. The root cause is that, existing mobility management is primarily based on wireless signal strength. While reasonable in static and low mobility, it is vulnerable to dramatic wireless dynamics from extreme mobility in triggering, decision, and execution. We devise REM, Reliable Extreme Mobility management for 4G, 5G, and beyond. REM shifts to movement-based mobility management in the delay-Doppler domain. Its signaling overlay relaxes feedback via cross-band estimation, simplifies policies with provable conflict freedom, and stabilizes signaling via scheduling-based OTFS modulation. Our evaluation with operational high-speed rail datasets shows that, REM reduces failures comparable to static and low mobility, with low signaling and latency cost.

The popularity of 4K videos has grown significantly in the past few years. Yet coding and streaming live 4K videos incurs prohibitive cost to the network and end system. Motivated by this observation, we explore the feasibility of supporting live 4K video streaming over wireless networks using commodity devices. Given the high data rate requirement of 4K videos, 60 GHz is appealing, but its large and unpredictable throughput fluctuation makes it hard to provide desirable user experience. In particular, to support live 4K video streaming, we should (i) adapt to highly variable and unpredictable wireless throughput, (ii) support efficient 4K video coding on commodity devices. To this end, we propose a novel system, Jigsaw. It consists of (i) easy-to-compute layered video coding to seamlessly adapt to unpredictable wireless link fluctuations, (ii) efficient GPU implementation of video coding on commodity devices, and (iii) effectively leveraging both WiFi and WiGig through delayed video adaptation and smart scheduling. Using real experiments and emulation, we demonstrate the feasibility and effectiveness of our system. Our results show that it improves PSNR by 6-15dB and improves SSIM by 0.011-0.217 over state-of-the-art approaches. Moreover, even when throughput fluctuates widely between 0.2Gbps-2Gbps, it can achieve an average PSNR of 33dB.

The citizen broadband radio service (CBRS) is a newly re-purposed spectrum band in 3550-3700 MHz, reclaiming spectrum occasionally used by radars and other incumbents for mobile data communication. It is also a poster child for future LTE-based dynamic spectrum access systems. At present, CBRS does not manage interference from unlicensed LTE users, which we show can be detrimental for its performance. In this paper we develop F-CBRS, a decentralized spectrum interference management system for unlicensed LTE users in the CBRS band. We first look at how much information can each operator be allowed to conceal and how much it has to be mandated (by a regulator) to disclose, and formally prove that the network can achieve fairness only if all operators share fully verifiable information about Access point (AP) locations and user activity. Using this insight we design a channel allocation scheme to efficiently utilize spectrum and incentivise collaboration. This also includes a simple, non-disruptive channel change scheme to frequently and efficiently change channels to accommodate dynamic traffic and environments. Through simulation and testbed evaluation, we show that we increase throughput of more than 90% of the flow by 80%-100% compared to the current CBRS protocol.

In this paper we study network architecture for unlicensed cellular networking for outdoor coverage in TV white spaces. The main technology proposed for TV white spaces is 802.11af, a Wi-Fi variant adapted for TV frequencies. However, 802.11af is originally designed for improved indoor propagation. We show that long links, typical for outdoor use, exacerbate known Wi-Fi issues, such as hidden and exposed terminal, and significantly reduce its efficiency.

Several data center transport protocols have been proposed in recent years (e.g., DCTCP, PDQ, and pFabric). In this paper, we first identify the underlying strategies used by the existing data center transports, namely, in-network Prioritization (used in pFabric), Arbitration (used in PDQ), and Self-adjusting at Endpoints (PASE) (used in DCTCP). We show that these strategies are complimentary to each other, rather than substitutes, as they have different strengths and can address each other's limitations. Unfortunately, prior data center transports use only one of these strategies. As a result, they either achieve near-optimal performance or deployment friendliness (i.e., require no changes to the data plane) but not both. Based on this insight, we design a data center transport protocol called PASE, which carefully synthesizes these strategies by assigning different transport responsibilities to each strategy. The key advantage of PASE over prior art is that it achieves both near-optimal performance as well as deployment friendliness. PASE does not require any changes in network switches (hardware or software); yet, it achieves comparable, or even better, performance than the state-of-the-art protocols (such as pFabric) that require changes to network elements. Our evaluation results show that the PASE performs well for a wide range of application workloads and network settings.

Many data center transports have been proposed in recent times (e.g., DCTCP, PDQ, pFabric, etc). Contrary to the common perception that they are competitors (i.e., protocol A vs. protocol B), we claim that the underlying strategies used in these protocols are, in fact, complementary. Based on this insight, we design PASE, a transport framework that synthesizes existing transport strategies, namely, self-adjusting endpoints (used in TCP style protocols), innetwork prioritization (used in pFabric), and arbitration (used in PDQ). PASE is deployment friendly: it does not require any changes to the network fabric; yet, its performance is comparable to, or better than, the state-of-the-art protocols that require changes to network elements (e.g., pFabric). We evaluate PASE using simulations and testbed experiments. Our results show that PASE performs well for a wide range of application workloads and network settings.

Electricity costs are a significant fraction of a cellular network's operations costs. We present Low-Carb, a practical scheme to decrease electrical energy consumption in operational cellular networks by coupling Base Transceiver Station (BTS) power savings with call hand-off—two features commonly used by cellular operators. Motivated by the practical observation that most callers are in the vicinity of multiple BTSs, Low-Carb presents and solves an optimization problem, allowing calls to hand-off from one BTS to another so that BTS power savings can be applied to a maximal number of BTSs throughout the cellular network. We use BTS locations and traffic volume data from a large live GSM network to evaluate the power savings possible using our proposed approach in Low-Carb. Our results indicate that for a GSM 1800 network operator with 7000 sites in an urban setting, a total of up to 35.36 MWh may be saved annually. This is at least 9.8% better than the energy savings obtained by just using BTS power savings alone. Other cellular operators can use the Low-Carb formulation with their own network data to estimate the electricity savings they may achieve on their networks.

The ability of tiny embedded devices to run large feature-rich programs is typically constrained by the amount of memory installed on such devices. Furthermore, the useful operation of these devices in wireless sensor applications is limited by their battery life. This paper presents a call stack redesign targeted at an efficient use of RAM storage and CPU cycles by a Java program running on a wireless sensor mote. Without compromising the application programs, our call stack redesign saves 30% of RAM, on average, evaluated over a large number of benchmarks. On the same set of bench-marks, our design also avoids frequent RAM allocations and deallocations, resulting in average 80% fewer memory operations and 23% faster program execution. These may be critical improvements for tiny embedded devices that are equipped with small amount of RAM and limited battery life. However, our call stack redesign is equally effective for any complex multi-threaded object oriented program developed for desktop computers. We describe the redesign, measure its performance and report the resulting savings in RAM and execution time for a wide variety of programs.