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.

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.

Video rate adaptation has large impact on quality of experience (QoE). However, existing video rate adaptation is rather limited due to a small number of rate choices, which results in (i) under-selection, (ii) rate fluctuation, and (iii) frequent rebuffering. Moreover, selecting a single video rate for a 360° video can be even more limiting, since not all portions of a video frame are equally important. To address these limitations, we identify new dimensions to adapt user QoE - dropping video frames, slowing down video play rate, and adapting different portions in 360° videos. These new dimensions along with rate adaptation give us a more fine-grained adaptation and significantly improve user QoE. We further develop a simple yet effective learning strategy to automatically adapt the buffer reservation to avoid performance degradation beyond optimization horizon. We implement our approach Favor in VLC, a well known open source media player, and demonstrate that Favor on average out-performs Model Predictive Control (MPC), rate-based, and buffer-based adaptation for regular videos by 24%, 36%, and 41%, respectively, and 2X for 360° videos.

The popularity of 360° videos has grown rapidly due to the immersive user experience. 360° videos are displayed as a panorama and the view automatically adapts with the head movement. Existing systems stream 360° videos in a similar way as regular videos, where all data of the panoramic view is transmitted. This is wasteful since a user only views a small portion of the 360° view. To save bandwidth, recent works propose the tile-based streaming, which divides the panoramic view to multiple smaller sized tiles and streams only the tiles within a user's field of view (FoV) predicted based on the recent head position. Interestingly, the tile-based streaming has only been simulated or implemented on desktops. We find that it cannot run in real-time even on the latest smartphone (e.g., Samsung S7, Samsung S8 and Huawei Mate 9) due to hardware and software limitations. Moreover, it results in significant video quality degradation due to head movement prediction error, which is hard to avoid. Motivated by these observations, we develop a novel tile-based layered approach to stream 360° content on smartphones to avoid bandwidth wastage while maintaining high video quality. Through real system experiments, we show our approach can achieve up to 69% improvement in user QoE and 49% in bandwidth savings over existing approaches. To the best of our knowledge, this is the first 360° streaming framework that takes into account the practical limitations of Android based smartphones.

With the availability of inexpensive and powerful drones, it is possible to let drones automatically follow a user for video taping. This can not only reduce cost, but also support video taping in situations where otherwise not possible (e.g., during private moments or at inconvenient locations like indoor rock climbing). While there have been many follow-me drones on the market for outdoors, which rely on GPS, enabling indoor follow-me function is more challenging due to the lack of an effective approach to track users in indoor environments. To this end, we develop a holistic system that lets a mobile phone carried by a user accurately track the drone's relative location and control it to maintain a specified distance and orientation for automatic video taping. We develop a series of techniques to (i) track a drone's location using acoustic signals with sub-centimeter errors even under strong propeller noise from the drone and complicated multipath in indoor environments, and (ii) solve practical challenges in applying model predictive control (MPC) framework to control the drone. The latter consists of developing measurement-based flight models, designing measurement techniques to provide feedback to the controller, and predicting the user's movement. We implement our system on AR Drone 2.0 and Samsung S7. The extensive evaluation shows that our drone can follow a user effectively and maintain a specified following distance and orientation within 2-3 cm and 1-3 degree errors, respectively. The videos taped by the drone during flight are smooth according to the jerk metric.

Video games, Virtual Reality (VR), Augmented Reality (AR), and Smart appliances (e.g., smart TVs) all call for a new way for users to interact and control them. This paper develops high-preCision Acoustic Tracker (CAT), which aims to replace a traditional mouse and let a user play games, interact with VR/AR headsets, and control smart appliances by moving a smartphone in the air. Achieving high tracking accuracy is essential to provide enjoyable user experience. To this end, we develop a novel system that uses audio signals to achieve mm-level tracking accuracy. It lets multiple speakers transmit inaudible sounds at different frequencies. Based on the received sound, our system continuously estimates the distance and velocity of the mobile with respect to the speakers to continuously track it. At its heart lies a distributed Frequency Modulated Continuous Waveform (FMCW) that can accurately estimate the absolute distance between a transmitter and a receiver that are separate and unsynchronized. We further develop an optimization framework to combine FMCW estimation with Doppler shifts and Inertial Measurement Unit (IMU) measurements to enhance the accuracy, and efficiently solve the optimization problem. We implement two systems: one on a desktop and another on a mobile phone. Our evaluation and user study show that our system achieves high tracking accuracy and ease of use using existing hardware.

Video games, virtual reality, augmented reality, and smart appliances all call for a new way for users to interact and control them. This paper develops high-preCision Acoustic Tracker (CAT), which aims to replace a traditional mouse and let a user control various devices by moving a smartphone in the air. At its heart lies a distributed Frequency Modulated Continuous Waveform (FMCW) that can accurately estimate the distance between a transmitter and a receiver that are separate and unsynchronized. We further develop an optimization framework to combine FMCW estimation with Doppler shifts to enhance the accuracy. We implement CAT on a mobile phone. The performance evaluation and user study show that our system achieves high tracking accuracy and ease of use using existing hardware.

The upsurge of cloud video recording (CVR) has gained increasing attention from the general public and entrepreneurs. With live video records archived in the cloud, the CVR paradigm enables various smart services by keeping track of activities in the monitored region from anywhere at any time. However, the limited upstream bandwidth affects the quality of surveillance when multiple distributed cameras share the same upstream link. To solve the problem, this paper proposes an efficient upstream bandwidth multiplexing algorithm to intelligently allocate upstream bandwidth for each live video stream while maximizing the overall utility from the perspective of a CVR user. Specifically, we formulate the upstream bandwidth multiplexing problem as a constrained stochastic optimization problem, and apply the technique of hierarchical approximation to solve it efficiently. Our algorithm can be extended to take the priority of video streams into account and allocate more upstream bandwidth to video streams with higher priorities. We explicitly prove the approximation ratio of the proposed algorithm. In addition, we also conduct extensive trace-driven simulations to verify the effectiveness of our algorithm. The simulation results show that our algorithm improves the overall CVR user utility by over 20% compared with other alternatives, and the average utility per bandwidth unit is guaranteed to be stable even when the number of video streams increases.

Cloud gaming has become a new trend for gamers to access high-end video games. By rendering games in the remote cloud and streaming video scenes to the users, games can be played anywhere, anytime, on any device (e.g., smartphones, tablets, or personal computers). In this paper, we address the problem of achieving cost-effective adaptive cloud gaming in geo-distributed data centers from the perspective of cloud gaming service providers (CGSPs). Unlike previous work, we consider a cloud gaming system supported with the adaptive streaming technology. Our purpose is to minimize the overall service cost for CGSPs, by adaptively adjusting the selection of data centers, virtual machine allocation and video bitrate configuration for each user. Meanwhile, we also need to ensure good-enough quality of experience (QoE) for gamers. To this objective, we formulate the problem into a constrained stochastic optimization problem, and apply the Lyapunov optimization theory to drive the corresponding online strategy with provable upper bounds. Due to the diverse QoE requirements of video games, we also take the difference among game genres into account during the algorithm design. Finally, we conduct extensive trace-driven simulations to evaluate the effectiveness of our algorithm and our results show that our proposed algorithm can achieve significant gain over other alternative approaches.

Cloud gaming has emerged as a promising approach to more affordable and accessible games. By rendering high-end video games in the cloud and streaming encoded game scenes to players via the broadband networks, users are relieved from downloading or installing game software. With cloud gaming, users can easily play high-end 3-D video games on any devices anytime and anywhere. In this paper, we conducted a comprehensive measurement study of a leading cloud gaming system in China, namely, CloudUnion. Unlike the previous work, our measurement study was based on an in-depth understanding of the internal mechanisms of CloudUnion, and thus we were able to reveal problems that cannot be observed in a black-box approach. We built a dedicated measurement platform, which enables us to study CloudUnion from different views, including the global view, local view, and user view. We also conducted a comparison study with another cloud gaming system, namely, GamingAnywhere. Our measurement results unveil the pros and cons of the current cloud gaming system design, and bring forth important insights about the cloud infrastructure, user behaviors, traffic patterns, user-perceived quality, and so on. Our work will be valuable for the design of future cloud gaming systems.

The emergence of cloud computing provides a cost-effective approach to deliver video streams to a large number of end users with the desired user quality of experience (QoE). Under such a paradigm, a video service provider (VSP) can launch its own video streaming services virtually by renting the distribution infrastructure from one or more cloud service providers (CSPs). However, CSPs such as Amazon EC2 normally offer multiple pricing options for virtual machine (VM) instances that they can provide, such as on-demand instances, reserved instances, and spot instances. Such diverse pricing models make it challenging for a VSP to determine how to optimally procure the required number of VM instances in different types to satisfy dynamic user demands. Given the limited budget, a VSP needs to carefully balance the procurement cost and the achieved QoE for end users. In this paper, we investigate the tradeoff between the cost incurred by VM instance procurement and the achieved QoE of end users under Amazon EC2's pricing models, and formulate the VM instance provisioning and procurement problem into a constrained stochastic optimization problem. By applying the Lyapunov optimization framework, we design an online procurement algorithm, which approaches the optimal solution with explicitly provable upper bounds. We also conduct extensive trace-driven simulations and our results show that our proposed algorithm (OPT-ORS) achieves a good balance between the procurement cost and the user QoE for cloud-based VSPs. In the achieved near-optimal situation, our algorithm guarantees that reserved VM instances are fully utilized to satisfy the baseline user demand, on-demand VM instances are only rented to handle flash crowds, while more spot VM instances are rented than on-demand VM instances to serve user demand over the baseline due to their low prices.

As a new paradigm, cloud gaming allows users to play high-end video games instantly without downloading or installing the original game software. In this paper, we first conduct a series of well-designed active and passive measurements on a large-scale cloud gaming platform and identify the significant diversity in the queueing delay and response delay among users. We note that the latency problem largely results from user-specified request routing and inelastic server provisioning. To address latency problem of the cloud gaming platform, we further propose an online control algorithm called iCloudAccess to perform intelligent request dispatching and server provisioning. Our main objective is to cut down the provisioning cost of cloud gaming service providers while still ensuring the user quality-of-experience requirements. We formulate the problem as a constrained stochastic optimization problem and apply the Lyapunov optimization theory to derive the online control algorithm with provable upper bounds. We also conduct extensive trace-driven simulations to evaluate the effectiveness of our algorithm, and our results show that our proposed algorithm achieves significant gain over other alternative approaches.

Mobile video traffic, owing to the rapid adoption of smartphones and tablets, has been growing exponentially in recent years and started to dominate the mobile Internet. In reality, mobile video applications commonly adopt buffering techniques to handle bandwidth fluctuation and minimize the impact of stochastic wireless channels on user experiences. However, recent measurement work reveals that mobile users tend to abort more frequently than PC users during viewing videos. Such a high abortion rate results in a significant wastage of buffered video data, which is directly translated into monetary and energy cost for mobile users. In this paper, we propose an intelligent buffer management strategy called CBM (Cost-aware Buffer Management), for mobile video streaming applications. Our purpose is to minimize cost induced by un-consumed video data while respecting certain user experience requirements. To this objective, we formulate the problem into a constrained stochastic optimization problem, and apply the Lyapunov optimization theory to derive the corresponding online strategy for cost minimization. Different from conventional heuristic-based strategies, our proposed CBM strategy can provide provably performance guarantee with explicit bounds. We also conduct extensive simulations to validate the effectiveness of our proposed strategy and our experimental results show that CBM achieves significant gains over existing schemes.

The prevalence of social networking services dramatically changes the landscape of video distribution, in which social video contents spread much faster than traditional video-sharing portals. The pervasive wireless connectivity further enables users to view and generate videos from anywhere at any time. In this paper, we focus on the problem of collaborative distribution of social videos in a wireless community cloud. We aim to minimize the total power consumption of all participants in the community. To this purpose, we first analyze the distribution problem using a Markovian model and study how the soft deadline threshold impacts the total power consumption. We derive the closed-form expression to reveal the relationship between the optimal power allocation strategy and the soft deadline threshold. Our numerical results show that the minimum power consumption increases convexly as the soft deadline threshold approaches one. Moreover, we also observe that when more paths are used for parallel transmission, the total power consumption increases in spite that the power consumption of each individual path is reduced.

For Internet video services, the high fluctuation of user demands in geographically distributed regions results in low resource utilizations of traditional content distribution network systems. Due to the capability of rapid and elastic resource provisioning, cloud computing emerges as a new paradigm to reshape the model of video distribution over the Internet, in which resources (such as bandwidth, storage) can be rented on demand from cloud data centers to meet volatile user demands. However, it is challenging for a video service provider (VSP) to optimally deploy its distribution infrastructure over multiple geo-distributed cloud data centers. A VSP needs to minimize the operational cost induced by the rentals of cloud resources without sacrificing user experience in all regions. The geographical diversity of cloud resource prices further makes the problem complicated. In this paper, we investigate the optimal deployment problem of cloud-assisted video distribution services and explore the best tradeoff between the operational cost and the user experience. We aim to pave the way for building the next-generation video cloud. Toward this objective, we first formulate the deployment problem into a min-cost network flow problem, which takes both the operational cost and the user experience into account. Then, we apply the Nash bargaining solution to solve the joint optimization problem efficiently and derive the optimal bandwidth provisioning strategy and optimal video placement strategy. In addition, we extend the algorithms to the online case and consider the scenario when peers participate into video distribution. Finally, we conduct extensive simulations to evaluate our algorithms in the realistic settings. Our results show that our proposed algorithms can achieve a good balance among multiple objectives and effectively optimize both operational cost and user experience.

Measurement studies of popular peer-to-peer (P2P) live video systems reveal that there exists extreme unfairness among peers in the swarm. Such kind of unfairness will provide disincentives to altruistic super peers and encourage free riding behavior in the system. It is essential for video service providers to take fairness into consideration when designing their systems. In this paper, we develop a simple model of P2P live video systems to understand the fairness problem from a theoretic perspective. We identify the fundamental tradeoff between fairness and performance, and propose a semidistributed algorithm based on the subgradient method to tune the P2P live video system toward optimal fairness while still maintaining the targeted universal streaming rate. We also conduct extensive trace-driven simulations to validate the effectiveness of our proposed algorithm. The simulation results show that our algorithm can guide the system toward optimal fairness quickly without degrading streaming performance at the same time.

To provide elastic cloud services with QoS guarantee, it is essential for cloud data centers to provision virtual machines rapidly according to user requests. Due to bandwidth bottleneck of centralized model, P2P model is recently adopted in data centers to relieve server workload by enabling sharing among VM instances. In this paper, we develop a simple theoretic model to analyze two typical P2P models for VM image distribution, namely, isolated-image P2P distribution model and cross-image P2P distribution model. We compare their efficiency under different parameter settings and derive their corresponding optimal server bandwidth allocation strategies. In addition, we also propose a practical optimal server bandwidth provisioning algorithm for chunk-level cross-image P2P distribution mechanism to further improve its efficiency. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm.