Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). Despite the abundance of recently proposed schemes, state-of-the-art ABR algorithms suffer from a key limitation: they use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and QoE objectives.We propose Pensieve, a system that generates ABR algorithms using reinforcement learning (RL). Pensieve trains a neural network model that selects bitrates for future video chunks based on observations collected by client video players. Pensieve does not rely on pre-programmed models or assumptions about the environment. Instead, it learns to make ABR decisions solely through observations of the resulting performance of past decisions. As a result, Pensieve automatically learns ABR algorithms that adapt to a wide range of environments and QoE metrics. We compare Pensieve to state-of-the-art ABR algorithms using trace-driven and real world experiments spanning a wide variety of network conditions, QoE metrics, and video properties. In all considered scenarios, Pensieve outperforms the best state-of-the-art scheme, with improvements in average QoE of 12%--25%. Pensieve also generalizes well, outperforming existing schemes even on networks for which it was not explicitly trained.

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Neural Adaptive Video Streaming with Pensieve

User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.

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A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

We present Chronos, a system that enables a single WiFi access point to localize clients to within tens of centimeters. Such a system can bring indoor positioning to homes and small businesses which typically have a single access point.

The key enabler underlying Chronos is a novel algorithm that can compute sub-nanosecond time-of-flight using commodity WiFi cards. By multiplying the time-of-flight with the speed of light, a MIMO access point computes the distance between each of its antennas and the client, hence localizing it. Our implementation on commodity WiFi cards demonstrates that Chronos's accuracy is comparable to state-of-the-art localization systems, which use four or five access points.

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Decimeter-level localization with a single WiFi access point

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates. The video player adaptively chooses the bitrate of each chunk that is downloaded, possibly choosing different bitrates for successive chunks. While bitrate adaptation holds the key to a good quality of experience for the user, current video players use ad-hoc algorithms that are poorly understood. We formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1/V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, our algorithm does not require any prediction of available network bandwidth. We empirically validate our algorithm in a simulated network environment using an extensive collection of network traces. We show that our algorithm achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and BOLA is part of the reference player implementation for the evolving DASH standard for video transmission.

BOLA: Near-optimal bitrate adaptation for online videos

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.

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A buffer-based approach to rate adaptation: evidence from a large video streaming service

To provide a better streaming experience, video clients today select their video rates by observing and estimating the available capacity. Recent work has shown that capacity estimation is fraught with difficulties because of complex interactions between the ABR control loop, HTTP server performance and TCP congestion control. Estimation-based rate selection algorithms can lead to unnecessary rebuffering events and suboptimal video quality. This paper argues that we should do away with estimating network capacity, and instead directly observe and control the playback buffer--which is the state variable we are most interested in controlling. We present a class of "buffer-based" rate selection algorithms that reduce the rebuffering rate while allowing us to control the delivered video quality. We implemented our algorithms inside the Netflix video client and ran a series of experiments spanning millions of Netflix users around the world. Our results show that by doing away with estimating network capacity and instead focusing on buffer occupancy, we can reduce rebuffer rates by 20% while holding video rate constant.

Using the Buffer to Avoid Rebuffers: Evidence from a Large Video Streaming Service

ObjectiveA data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic.

MethodsThe pandemic response commons was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the pandemic response commons.

ResultsWe developed a general pandemic response commons and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures and controls based upon NIST SP 800-53. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data.

Discussion and conclusionGiven the various legal and data agreements required to operate a data commons, a pandemic response commons is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a Pandemic Response Commons is designed to be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing of regional data.

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The Pandemic Response Commons

Finding, accessing, sharing, and analyzing patient data from a clinical setting for collaborative research has continually proven to be a challenge in healthcare organizations. The human and technological architecture required to perform these services exist at the largest academic institutions but are usually under-funded. At smaller, less academically focused healthcare organizations across the United States, where the majority of care is delivered, they are generally absent. Here we propose a solution called the Learning Healthcare System Data Commons where cost is usage-based and the most basic elements are designed to be extensible, allowing it to evolve with the changing landscape of healthcare. Herein we also discuss our reference implementation of this platform tailored specifically for operational sustainability and governance using the data generated in a hospital setting for research, quality, and educational purposes.

Matthew Trunnell

Papers

A buffer-based approach to rate adaptation: evidence from a large video streaming service

Using the Buffer to Avoid Rebuffers: Evidence from a Large Video Streaming Service

The Pandemic Response Commons

Economical Utilization of Health Information with Learning Healthcare System Data Commons.