Swiping the screen to switch videos is a unique browsing behavior for short videos, intended to facilitate viewers in quickly searching for content of interest. However, frequent video switching can result in nearly half of the data being used to transmit never-watched video data, leading to unnecessary network load via resource wastage. To tackle this problem, recent studies have utilized historical viewing data to predict the necessary length of videos to download based on viewing probability. Critically, the precision of the predictions plays a pivotal role in shaping both data consumption and user experience. This paper addresses issues that emerge with inaccurate predictions by proposing KBL (Kettle-style Buffer Loading), a novel algorithm to balance waste with a high-quality video experience, without requiring extensive training or prior knowledge. Inspired by tea kettle service, KBL reduces waste by setting the boundary of the respective video buffers, current and pre-loaded videos, based on an evaluation of the network conditions. Through extensive evaluation, KBL is demonstrated to reduce waste by up to 58% of data usage compared to state of the art short video strategies without incurring significant QoE degradation, even in the face of shifting user behavior.

Network throughput tests, commonly known as “speed tests” are widely used by consumers, regulators, and ISPs to measure and diagnose network performance. However, the tools used to conduct these tests are often costly in terms of data consumption. Moreover, the speed tests rely on data that is transferred to clients for the sole purpose of measuring throughput with the data being discarded and serving no other purpose. In this paper, we present rePurpose, a system that moves useful content (ads) to enable periodic speed tests by significantly offsetting the cost of network measurement, thereby avoiding harming user QoE. rePurpose can work within the existing ad ecosystem to pre-stage ads needed by users ahead of time. We evaluate the efficacy of rePurpose by emulating a common scenario where users watch videos and ads. Our evaluation shows that rePurpose can reduce the data cost of periodic speed tests by up to 90%. Moreover, by virtue of the time-shifted delivery courtesy of the periodic speed tests moving useful data, rePurpose improves video and ad QoE by reducing or eliminating start-up delay by up to five seconds.

Active network probing, commonly known as a speed test, is the prevalent network speed measurement and diagnostic method. Speed tests primarily measure achievable throughput by conducting bulk downloads that saturate the bottleneck link. However, the impact of speed tests on user Quality of Experience (QoE) has not been thoroughly explored. In this paper, we investigate the effects of active network probing on user QoE during two common activities: file downloading and video streaming, focusing on key QoE metrics such as download time, video bitrate, and buffering. Our analysis reveals that the standard speed test significantly extends download times (by up to 88% in WiFi and 46% in cellular networks) and adversely affects various video QoE metrics, particularly bitrate, resulting in an average bitrate reduction ranging from 46% to 60%. Moreover, we assess the outcomes of typical speed test scenarios, such as single and double tests, and establish that both variants impair QoE, with double tests causing greater disruptions. Our findings offer a comprehensive insight into the ramifications of active network probing on user applications and emphasize the necessity for approaches to alleviate its detrimental effects on QoE.

Short videos have recently emerged as a popular form of short-duration User Generated Content (UGC) within modern social media. Short video content is generally less than a minute long and predominantly produced in vertical orientation on smartphones. While still fundamentally being streaming, short video delivery is distinctly characterized by the deployment of a mechanism that pre-loads ahead of user request. Background pre-loading aims to eliminate start-up time, which is now prioritized higher in Quality of Experience (QoE) objectives, given that the application design facilitates instant 'swiping' to the next video in a recommended sequence. In this work, we provide a comprehensive comparison of four popular short video services. In particular, we explore content characteristics and evaluate the video quality across resolutions for each service. We next characterize the pre-loading policy adopted by each service. Last, we conduct an experimental study to investigate data consumption and evaluate achieved QoE under different network scenarios and application configurations.

Histograms have a large variety of useful applications in data analysis, e.g., tracking the spread of diseases and analyzing public health issues. However, most data analysis techniques used in practice operate over plaintext data, putting the privacy of users’ data at risk. We consider the problem of allowing an untrusted aggregator to privately compute a histogram over multiple users’ private inputs (e.g., number of contacts at a place) without learning anything other than the final histogram. This is a challenging problem to solve when the aggregators and the users may be malicious and collude with each other to infer others’ private inputs, as existing black box techniques incur high communication and computational overhead that limit scalability. We address these concerns by building a novel, efficient, and scalable protocol that intelligently combines a Trusted Execution Environment (TEE) and the Durstenfeld-Knuth uniformly random shuffling algorithm to update a mapping between buckets and keys by using a deterministic cryptographically secure pseudorandom number generator. In addition to being provably secure, experimental evaluations of our technique indicate that it generally outperforms existing work by several orders of magnitude, and can achieve performance that is within one order of magnitude of protocols operating over plaintexts that do not offer any security.

In the past few years, unmanned aerial vehicles (UAVs) have drastically increased in popularity both from consumer and industry perspectives. A key component towards enabling the widespread usage of UAVs is the ability to stay in near-constant communication with the drone for command and control and conveying relevant instrumentation. The usage of cellular technology, namely LTE, seems to be a natural fit for addressing coverage and Line of Sight (LoS) issues. However, there is a relative dearth of data, specifically open source data that explores key performance aspects of cellular at altitudes typically envisioned for commercial UAV operation. The key contribution of this paper is to analyze data taken from numerous drone flights that include varying altitudes, locations, and multiple cellular carriers as recorded in a medium-sized Midwestern city. Further, we offer our data as an open-source repository for the community offering multiple vantage points for the various runs including the operating system, chipset (through MobileInsight), drone instrumentation, and server-side packet captures as part of the recorded data streams.

Cellular networks provide an essential connectivity foundation for a sizable number of mobile devices and applications, making it compelling to measure their performance in regard to user experience. Although cellular infrastructure provides low-level mechanisms for network-specific performance measurements, there is still a distinct gap in discerning the actual application-level or user-perceivable performance from such methods. Put simply, there is little substitute for direct sampling and testing to measure end-to-end performance. Unfortunately, most existing technologies often fall quite short. Achievable Throughput tests use bulk TCP downloads to provide an accurate but costly (time, bandwidth, energy) view of network performance. Conversely, Available Bandwidth techniques offer improved speed and low cost but are woefully inaccurate when faced with the typical dynamics of cellular networks. In this paper, we propose CUP, a novel approach for Cellular Ultra-light Probe-based available bandwidth estimation that seeks to operate at the cost point of Available Bandwidth techniques while correcting accuracy issues by leveraging the intrinsic aggregation properties of cellular scheduling, coupled with intelligent packet timing trains and the application of Bayesian probabilistic analysis. By keeping the costs low with reasonable accuracy, our approach enables scaling both with respect to time (longitude) and space (user device density). We construct a CUP prototype to evaluate our approach under various demanding real-world cellular environments (longitudinal, driving, multiple vendors) to demonstrate the efficacy of our approach.

The advancement of the Internet of Things (IoT) is bringing unprecedented convenience into our daily life. However, with the relentlessly increasing number of mobile devices connected to the Internet, the wireless network environment is becoming more crowded than ever before. Particularly, WiFi, with its evolving role in IoT, is shouldering a tremendous amount of traffic from IoT and other mobile devices. As a result, exploding numbers of competing devices, encroachment by cellular technology, and dramatic increases in content richness deliver a more variable Quality of Experience (QoE) on WiFi than desired. Moreover, such variance tends to occur both across time and space making it an extremely difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to be too narrow. To conduct efficient and nonobtrusive WiFi traffic characterization, in this article, we propose a novel passive client-side approach that delivers efficient and accurate characterization by taking advantage of the properties of frame aggregation (FA) and block acknowledgment (BA). The devised approach requires only capturing and analyzing certain types of control packets thus making it feasible to deploy on IoT devices that have limited computation power. We show in this article that we can accurately derive important characterization metrics, such as airtime, queuing information, and transmission rates with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.

WiFi has emerged as a pivotal technology for delivering Quality of Experience (QoE) to mobile devices. Unfortunately, exploding numbers of competing devices, potential encroachment by cellular technology, and dramatic increases in content richness deliver a more variable QoE than desired. Moreover, such variance tends to occur both across time and space making it a difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to suffer from accuracy issues. In our paper, we propose a novel passive client-side approach that provides an efficient and accurate characterization by taking advantage of the properties of Frame Aggregation (FA) and Block Acknowledgements (BA). We show in the paper that one can accurately derive important metrics such as airtime and throughput with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.

Video streaming using WiFi networks poses the challenge of variable network performance when multiple clients are present. Hence, it is important to continuously monitor and predict the network changes in order to ensure a higher user quality of experience (QoE) for video streaming. Existing approaches that aim to detect such network changes have several disadvantages. For example, active probing approaches are expensive so that generate more additional traffic flow during the testing. To overcome its shortcomings, we propose a passive, lightweight approach, CP-DASH, whereby queuing effects present in frame aggregation are leveraged to predict link congestion in the WiFi network. This approach allows the early detection which can be used to adapt our video appropriately. We conduct experiments simulating a WiFi network with multiple clients and compare CP-DASH with five contemporary rate selection mechanisms. We found that our proposed method significantly reduces the switch rates and stall rates from 22% to 5% and from 38% to 25% compared with an existing throughput-based algorithm, respectively.