A Measurement Study of Live 360 Video Streaming Systems

360-degree live video streaming is becoming increasingly popular. While providing viewers with enriched experience, 360-degree live video streaming is challenging to achieve since it requires a significantly higher bandwidth and a powerful computation infrastructure. A deeper understanding of this emerging system would benefit both viewers and system designers. Although prior works have extensively studied regular video streaming and 360-degree video on demand streaming, we for the first time investigate the performance of 360-degree live video streaming. We conduct a systematic measurement of YouTube’s 360-degree live video streaming using various metrics in multiple practical settings. Our research insight will help to build a clear understanding of today’s 360-degree live video streaming and lay a foundation for future research on this emerging yet relatively unexplored area. To further understand the delay measured in YouTube’s 360-degree live video streaming, we conduct the second measurement study on a 360-degree live video streaming platform. While live 360-degree video streaming provides an enriched viewing experience, it is challenging to guarantee the user experience against the negative effects introduced by start-up delay, event-to-eye delay, and low frame rate. It is therefore imperative to understand how different computing tasks of a live 360-degree streaming system contribute to these three delay metrics. Our measurement provide insights for future research directions towards improving the user experience of live 360-degree video streaming. Based on our measurement results, we propose a motion-based trajectory transmission method for 360-degree video streaming. First, we design a testbed for 360-degree video playback. The testbed can collect the users viewing data in real time. Then we analyze the trajectories of the moving targets in the 360-degree videos. Specifically, we utilize optical flow algorithms and gaussian mixture model to pinpoint the trajectories. Then we choose the trajectories to be delivered based on the size of the moving targets. The experiment results indicates that our method can obviously reduce the bandwidth consumption

Use of Cloud Gaming in Education

The use of digital games in education has been the subject of research for many years and their usefulness has been confirmed by many studies and research projects. Standardized tests, such as PISA test, show that respondents achieved better reading, math and physics results if they used the computer more for gaming-related activities. It has been proven that the application of video games in education increases student motivation, improves several types of key skills—social and intellectual skills, reflexes and concentration. Nevertheless, there are several challenges associated with the application of video games in schools and they can be categorized as technical (network and end device limitations), competency (teachers’ knowledge in the area), qualitative (lack of educational games of high quality), and financial (high cost of purchasing games and equipment). The novel architecture for delivery of gaming content commonly referred to as “cloud gaming” has the potential to solve most of the present challenges of using games in education. A well-designed cloud gaming platform would enable seamless and simple usage for both students and teachers. While solving most of the present problems, cloud gaming introduces a set of new research challenges which will be discussed in this section

Cloud-based or On-device: An Empirical Study of Mobile Deep Inference

Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values. Because of computational complexity and size constraints, these trained models are often hosted in the cloud. To utilize these cloud-based models, mobile apps will have to send input data over the network. While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have network access. With mobile specific deep learning optimizations, it is now possible to employ on-device inference. However, because mobile hardware, such as GPU and memory size, can be very limited when compared to its desktop counterpart, it is important to understand the feasibility of this new on-device deep learning inference architecture. In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks (CNNs) using a benchmark Android application we developed. Our measurement and analysis suggest that on-device inference can cost up to two orders of magnitude greater response time and energy when compared to cloud-based inference, and that loading model and computing probability are two performance bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering (IC2E) conference 201

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

Tackling mobile traffic critical path analysis with passive and active measurements

Critical Path Analysis (CPA) studies the delivery of webpages to identify page resources, their interrelations, as well as their impact on the page loading latency. Despite CPA being a generic methodology, its mechanisms have been applied only to browsers and web traffic, but those do not directly apply to study generic mobile apps. Likewise, web browsing represents only a small fraction of the overall mobile traffic. In this paper, we take a first step towards filling this gap by exploring how CPA can be performed for generic mobile applications. We propose Mobile Critical Path Analysis (MCPA), a methodology based on passive and active network measurements that is applicable to a broad set of apps to expose a fine-grained view of their traffic dynamics. We validate MCPA on popular apps across different categories and usage scenarios. We show that MCPA can identify user interactions with mobile apps only based on traffic monitoring, and the relevant network activities that are bottlenecks. Overall, we observe that apps spend 60% of time and 84% of bytes on critical traffic on average, corresponding to +22% time and +13% bytes than what observed for browsing

Measuring the Latency of Graphics Frameworks on X11 Based Systems

Latency is an intrinsic property of all human-computer systems. As it can affect user experience and performance, it should be kept as low as possible for real-time applications. To identify the source of latency, measuring partial latencies is necessary. We present a new method for measuring the latency of graphics frameworks on X11-based systems. Our tool measures the time between an input event arriving at the kernel until a pixel is updated in graphics memory. In a systematic evaluation with 36 test applications, we found that our method delivers consistent results for most tested frameworks, and does not add a significant amount of additional end-to-end latency. Even though further investigation is required to explain inconsistencies with Qt-based frameworks, our method measures the latency of graphics frameworks reliably and accurately in all other cases