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1. VisualWorldDB: A DBMS for the Visual World. Brandon Haynes, Maureen Daum, Amrita Mazumdar, Magdalena Balazinska, Alvin Cheung, and Luis Ceze. CIDR, 2020.

   Brandon Haynes, Maureen Daum (January 2020, Conference on Innovative Data Systems Research (CIDR))

   null (Ed.)

   Many recent video applications |including autonomous driving, traffic monitoring, drone analytics, large-scale surveillance networks, and virtual reality require reasoning about, combining, and operating over many video streams, each with distinct position and orientation. However, modern video data management systems are largely designed to process individual streams of video data as if they were independent and unrelated. In this paper, we present VisualWorldDB, a vision and an initial architecture for a new type of database management system optimized for multi-video applications. VisualWorldDB ingests video data from many perspectives and makes them queryable as a single multidimensional visual object. It incorporates new techniques for optimizing, executing, and storing multi-perspective video data. Our preliminary results suggest that this approach allows for faster queries and lower storage costs, improving the state of the art for applications that operate over this type of video data. 

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2. Requet: Real-Time QoE Detection for Encrypted YouTube Traffic

   https://doi.org/10.1145/3304109.3306226  

   Craig Gutterman†, Katherine Guo‡ (January 2019, Multimedia systems)

   As video traffic dominates the Internet, it is important for operators to detect video Quality of Experience (QoE) in order to ensure adequate support for video traffic. With wide deployment of endto- end encryption, traditional deep packet inspection based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a Machine Learning (ML) algorithm to predict QoE metrics, specifically, buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively. 

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3. Sprocket: A Serverless Video Processing Framework

   https://doi.org/10.1145/3267809.3267815  

   Ao, Lixiang; Izhikevich, Liz; Voelker, Geoffrey M.; Porter, George (October 2018, Proceedings of the ACM Symposium on Cloud Computing (SoCC ’18))

   Sprocket is a highly configurable, stage-based, scalable, serverless video processing framework that exploits intra-video parallelism to achieve low latency. Sprocket enables developers to program a series of operations over video content in a modular, extensible manner. Programmers implement custom operations, ranging from simple video transformations to more complex computer vision tasks, in a simple pipeline specification language to construct custom video processing pipelines. Sprocket then handles the underlying access, encoding and decoding, and processing of video and image content across operations in a highly parallel manner. In this paper we describe the design and implementation of the Sprocket system on the AWS Lambda serverless cloud infrastructure, and evaluate Sprocket under a variety of conditions to show that it delivers its performance goals of high parallelism, low latency, and low cost (10s of seconds to process a 3,600 second video 1000-way parallel for less than $3). 

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4. Optimal Strategies for Live Video Streaming in the Low-latency Regime

   https://doi.org/10.1109/ICNP.2019.8888127  

   Sun, Liyang; Zong, Tongyu; Liu, Yong; Wang, Yao; Zhu, Haihong (October 2019, IEEE 27th International Conference on Network Protocols (ICNP))

   Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live video streaming by developing dynamic models and optimal control strategies. We further develop practical live video streaming algorithms within the Model Predictive Control (MPC) framework, namely MPC-Live, to maximize user QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live video streaming algorithms can improve the performance dramatically within latency range of two to five seconds. 

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5. Video analytics from edge to server: work-in-progress

   Jiashen Cao, Ramyad Hadidi (January 2019, CODES/ISSS '19: Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion)

   Deep learning algorithms are an essential component of video analytics systems, in which the content of a video stream is analyzed. Although numerous studies target optimizing server-based video analysis, partially processing videos on edge devices is beneficial. Since edge devices are closer to data, they deliver initial insights on data before sending it to cloud. In this paper, we present an edge-tailored video analytics system by using a multi-stage network designed to run on heterogeneous computing resources. 

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