Measuring and Improving the Quality of Experience of Adaptive Rate Video

Today's popular over-the-top (OTT) video streaming services such as YouTube, Netflix and Hulu deliver video contents to viewers using adaptive bitrate (ABR) technologies. In ABR streaming, a video player running on a viewer's device adaptively changes bitrates to match given network conditions. However, providing reliable streaming is challenging. First, an ABR player may select an inappropriate bitrate during playback due to the lack of direct knowledge of access networks, frequent user mobility and rapidly changing channel conditions. Second, OTT content is delivered to viewers without any cooperation with Internet service providers (ISPs). Last, there are no appropriate tools that evaluate the performance of ABR streaming along with video quality of experience (QoE). This thesis describes how to improve the video QoE of OTT video streaming services using ABR technologies. Our analysis starts from understanding ABR heuristics. How does ABR streaming work? What factors does an ABR player consider when switching bitrates during a download? Then, we propose our solutions to improve existing ABR streaming from the perspective of network operators who deliver video content through their networks and video service providers who build ABR players running on viewers' devices. From the network operators' point of view, we propose to find a better video content server based on round trip times (RTTs) between an edge node of a wireless network and available video content servers when a viewer requests a video. The edge node can be an Internet Service Provider (ISP) router in a Wi-Fi network and a packet data network gateway (P-GW) in a 4G network. During the experiments, our solution showed better TCP performance (e.g., higher TCP throughput during playback) 146 times out of 200 experiments (73%) over Wi-Fi networks and 162 times out of 200 experiments (81%) over 3G networks. In addition, we claim that the wireless edge nodes can assist an ABR video player in selecting the best available bitrate by controlling the available bandwidth in the radio access network between a base station and a viewer's device. In our Wi-Fi testbed, the proposed solution saved up to 21% of radio bandwidth on mobile devices and enhanced the viewing experience by reducing rebufferings during playback. Last, we assert that software-defined networking (SDN) can improve video QoE by dynamically controlling routing paths of video streaming flows based on the provisioned networking information collected from SDN-enabled networking devices. Using an off-the-shelf SDN platform, we showed that our proposed solution can reduce rebufferings by 50% and provide higher bitrates during a download. From the perspective of video service providers, higher video QoE can be achieved by improving ABR heuristics implemented in an ABR player. To support this idea, we investigated the role of playout buffer size in ABR streaming and its impact on video QoE. Through our video QoE survey, we proved that a large buffer does not always outperform a small buffer, especially under rapidly varying network conditions. Based on this finding, we suggest to dynamically change the maximum buffer size in an ABR player depending on the current capacity of its playout buffer for improving the QoE of viewers. During the experiments, our proposed solution improved the viewing experience by offering 15% higher average played bitrate, 70% fewer bitrate changes and 50% shorter rebuffering duration. Our experimental results show that even small changes of ABR heuristics and new features of network systems can greatly affect video QoE. However, it is still difficult for video service providers or network operators to evaluate new ABR heuristics or network system changes due to lack of accurate QoE monitoring systems. In order to solve this issue, we have developed YouSlow ("YouTube Too Slow!? - YouSlow") as a new approach to monitoring video QoE for the analysis of ABR performance. The lightweight web browser plug-in and mobile application are designed to monitor various playback events (e.g., rebuffering duration and frequency of bitrate changes) directly from within ABR video players and calculate statistics along with video QoE. Using YouSlow, we investigate the impact of the above playback events on video abandonment: about 10% of viewers abandoned the YouTube videos when the pre-roll ads lasted for 15 seconds. Even increasing the bitrate can annoy viewers; they prefer a high starting bitrate with no bitrate changes during playback. Our regression analysis shows that bitrate changes do not affect video abandonment significantly and the abandonment rate can be estimated accurately using the rebuffering ratio and the number of rebufferings. The thesis includes four main contributions. First, we investigate today's popular OTT video streaming services (e.g., YouTube and Netflix) that use ABR streaming technologies. Second, we propose to build QoS and QoE aware video streaming that can be implemented in existing wireless networks (e.g., Wi-Fi, 3G and 4G) and in SDN-enabled networks. Third, we propose to improve current ABR heuristics by dynamically changing the playout buffer size under varying network conditions. Last, we designed and implemented a new monitoring system for measuring video QoE

Connecting the Physical World with Arduino in SECE

The Internet of Things (IoT) enables the physical world to be connected and controlled over the Internet. This paper presents a smart gateway platform that connects everyday objects such as lights, thermometers, and TVs over the Internet. The proposed hardware architecture is implemented on an Arduino platform with a variety of off the shelf home automation technologies such as Zigbee and X10. Using the microcontroller-based platform, the SECE (Sense Everything, Control Everything) system allows users to create various IoT services such as monitoring sensors, controlling actuators, triggering action events, and periodic sensor reporting. We give an overview of the Arduino-based smart gateway architecture and its integration into SECE

WiSlow: A WiFi Network Performance Troubleshooting Tool for End Users

The increasing number of 802.11 APs and wireless devices results in more contention, which causes unsatisfactory WiFi network performance. In addition, non-WiFi devices sharing the same spectrum with 802.11 networks such as microwave ovens, cordless phones, and baby monitors severely interfere with WiFi networks. Although the problem sources can be easily removed in many cases, it is difficult for end users to identify the root cause. We introduce WiSlow, a software tool that diagnoses the root causes of poor WiFi performance with user-level network probes and leverages peer collaboration to identify the location of the causes. We elaborate on two main methods: packet loss analysis and 802.11 ACK pattern analysis

Mobile Video Is Inefficient: A Traffic Analysis

Video streaming on mobile devices is on the rise. According to recent reports, mobile video streaming traffic accounted for 52.8% of total mobile data traffic in 2011, and it is forecast to reach 66.4% in 2015. We analyzed the network traffic behaviors of the two most popular HTTP-based video streaming services: YouTube and Netflix. Our research indicates that the network traffic behavior depends on factors such as the type of device, multimedia applications in use and network conditions. Furthermore, we found that a large part of the downloaded video content can be unaccepted by a video player even though it is successfully delivered to a client. This unwanted behavior often occurs when the video player changes the resolution in a fluctuating network condition and the playout buffer is full while downloading a video. Some of the measurements show that the discarded data may exceed 35% of the total video content

Towards Dynamic Network Condition-Aware Video Server Selection Algorithms over Wireless Networks

We investigate video server selection algorithms in a distributed video-on-demand system. We conduct a detailed study of the YouTube Content Delivery Network (CDN) on PCs and mobile devices over Wi-Fi and 3G networks under varying network conditions. We proved that a location-aware video server selection algorithm assigns a video content server based on the network attachment point of a client. We found out that such distance-based algorithms carry the risk of directing a client to a less optimal content server, although there may exist other better performing video delivery servers. In order to solve this problem, we propose to use dynamic network information such as packet loss rates and Round Trip Time (RTT)between an edge node of an wireless network (e.g., an Internet Service Provider (ISP) router in a Wi-Fi network and a Radio Network Controller (RNC) node in a 3G network) and video content servers, to find the optimal video content server when a video is requested. Our empirical study shows that the proposed architecture can provide higher TCP performance, leading to better viewing quality compared to location-based video server selection algorithms

Towards A Dynamic QoS-aware Over-The-Top Video Streaming in LTE

We present a study of traffic behavior of two popular over-the-top (OTT) video streaming services (YouTube and Netflix). Our analysis is conducted on different mobile devices (iOS and Android) over various wireless networks (Wi-Fi, 3G and LTE) under dynamic network conditions. Our measurements show that the video players frequently discard a large amount of video content although it is successfully delivered to a client. We first investigate the root cause of this unwanted behavior. Then, we propose a Quality-of-Service (QoS)-aware video streaming architecture in Long Term Evolution (LTE) networks to reduce the waste of network resource and improve user experience. The architecture includes a selective packet discarding mechanism, which can be placed in packet data network gateways (P-GW). In addition, our QoS-aware rules assist video players in selecting an appropriate resolution under a fluctuating channel condition. We monitor network condition and configure QoS parameters to control availability of the maximum bandwidth in real time. In our experimental setup, the proposed platform shows up to 20.58% improvement in saving downlink bandwidth and improves user experience by reducing buffer underflow period to an average of 32 seconds

Enhancing State Estimator for Autonomous Race Car : Leveraging Multi-modal System and Managing Computing Resources

This paper introduces an innovative approach to enhance the state estimator for high-speed autonomous race cars, addressing challenges related to unreliable measurements, localization failures, and computing resource management. The proposed robust localization system utilizes a Bayesian-based probabilistic approach to evaluate multimodal measurements, ensuring the use of credible data for accurate and reliable localization, even in harsh racing conditions. To tackle potential localization failures during intense racing, we present a resilient navigation system. This system enables the race car to continue track-following by leveraging direct perception information in planning and execution, ensuring continuous performance despite localization disruptions. Efficient computing resource management is critical to avoid overload and system failure. We optimize computing resources using an efficient LiDAR-based state estimation method. Leveraging CUDA programming and GPU acceleration, we perform nearest points search and covariance computation efficiently, overcoming CPU bottlenecks. Real-world and simulation tests validate the system's performance and resilience. The proposed approach successfully recovers from failures, effectively preventing accidents and ensuring race car safety.Comment: arXiv admin note: text overlap with arXiv:2207.1223

Capnography for Assessing Nocturnal Hypoventilation and Predicting Compliance with Subsequent Noninvasive Ventilation in Patients with ALS

BACKGROUND: Patients with amyotrophic lateral sclerosis (ALS) suffer from hypoventilation, which can easily worsen during sleep. This study evaluated the efficacy of capnography monitoring in patients with ALS for assessing nocturnal hypoventilation and predicting good compliance with subsequent noninvasive ventilation (NIV) treatment. METHODS: Nocturnal monitoring and brief wake screening by capnography/pulse oximetry, functional scores, and other respiratory signs were assessed in 26 patients with ALS. Twenty-one of these patients were treated with NIV and had their treatment compliance evaluated. RESULTS: Nocturnal capnography values were reliable and strongly correlated with the patients' respiratory symptoms (R(2) = 0.211-0.305, p = 0.004-0.021). The duration of nocturnal hypercapnea obtained by capnography exhibited a significant predictive power for good compliance with subsequent NIV treatment, with an area-under-the-curve value of 0.846 (p = 0.018). In contrast, no significant predictive values for nocturnal pulse oximetry or functional scores for nocturnal hypoventilation were found. Brief waking supine capnography was also useful as a screening tool before routine nocturnal capnography monitoring. CONCLUSION: Capnography is an efficient tool for assessing nocturnal hypoventilation and predicting good compliance with subsequent NIV treatment of ALS patients, and may prove useful as an adjunctive tool for assessing the need for NIV treatment in these patients

Longitudinal evolution of cortical thickness signature reflecting Lewy body dementia in isolated REM sleep behavior disorder: a prospective cohort study

Background The isolated rapid-eye-movement sleep behavior disorder (iRBD) is a prodromal condition of Lewy body disease including Parkinson's disease and dementia with Lewy bodies (DLB). We aim to investigate the longitudinal evolution of DLB-related cortical thickness signature in a prospective iRBD cohort and evaluate the possible predictive value of the cortical signature index in predicting dementia-first phenoconversion in individuals with iRBD. Methods We enrolled 22 DLB patients, 44 healthy controls, and 50 video polysomnography-proven iRBD patients. Participants underwent 3-T magnetic resonance imaging (MRI) and clinical/neuropsychological evaluations. We characterized DLB-related whole-brain cortical thickness spatial covariance pattern (DLB-pattern) using scaled subprofile model of principal components analysis that best differentiated DLB patients from age-matched controls. We analyzed clinical and neuropsychological correlates of the DLB-pattern expression scores and the mean values of the whole-brain cortical thickness in DLB and iRBD patients. With repeated MRI data during the follow-up in our prospective iRBD cohort, we investigated the longitudinal evolution of the cortical thickness signature toward Lewy body dementia. Finally, we analyzed the potential predictive value of cortical thickness signature as a biomarker of phenoconversion in iRBD cohort. Results The DLB-pattern was characterized by thinning of the temporal, orbitofrontal, and insular cortices and relative preservation of the precentral and inferior parietal cortices. The DLB-pattern expression scores correlated with attentional and frontal executive dysfunction (Trail Making Test-A and B: R = − 0.55, P = 0.024 and R = − 0.56, P = 0.036, respectively) as well as visuospatial impairment (Rey-figure copy test: R = − 0.54, P = 0.0047). The longitudinal trajectory of DLB-pattern revealed an increasing pattern above the cut-off in the dementia-first phenoconverters (Pearsons correlation, R = 0.74, P = 6.8 × 10−4) but no significant change in parkinsonism-first phenoconverters (R = 0.0063, P = 0.98). The mean value of the whole-brain cortical thickness predicted phenoconversion in iRBD patients with hazard ratio of 9.33 [1.16–74.12]. The increase in DLB-pattern expression score discriminated dementia-first from parkinsonism-first phenoconversions with 88.2% accuracy. Conclusion Cortical thickness signature can effectively reflect the longitudinal evolution of Lewy body dementia in the iRBD population. Replication studies would further validate the utility of this imaging marker in iRBD