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.

/pdf/neural-adaptive-video-streaming-with-pensieve-3y10qvjqj1.pdf

Neural Adaptive Video Streaming with Pensieve

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.

/pdf/a-buffer-based-approach-to-rate-adaptation-evidence-from-a-42j7jpd30s.pdf

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

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.

/pdf/a-control-theoretic-approach-for-dynamic-adaptive-video-1mmix9lrz0.pdf

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

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

A Survey on Quality of Experience of HTTP Adaptive Streaming

A system and method in a building or vehicle for an actuator operation in response to a sensor according to a control logic, the system comprising a router or a gateway communicating with a device associated with the sensor and a device associated with the actuator over in-building or in-vehicle networks, and an external Internet-connected control server associated with the control logic implementing a PID closed linear control loop and communicating with the router over external network for controlling the in-building or in-vehicle phenomenon. The sensor may be a microphone or a camera, and the system may include voice or image processing as part of the control logic. A redundancy is used by using multiple sensors or actuators, or by using multiple data paths over the building or vehicle internal or external communication. The networks may be wired or wireless, and may be BAN, PAN, LAN, WAN, or home networks.

System and method for server based control

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Bufferbloat is a phenomenon where excess buffers in the network cause high latency and jitter. As more and more interactive applications (e.g. voice over IP, real time video conferencing and financial transactions) run in the Internet, high latency and jitter degrade application performance. There is a pressing need to design intelligent queue management schemes that can control latency and jitter; and hence provide desirable quality of service to users. We present here a lightweight design, PIE (Proportional Integral controller Enhanced), that can effectively control the average queueing latency to a reference value. The design does not require per-packet extra processing, so it incurs very small overhead and is simple to implement in both hardware and software. In addition, the design parameters are self-tuning, and hence PIE is robust and optimized for various network scenarios. Simulation results, theoretical analysis and Linux testbed results show that PIE can ensure low latency and achieve high link utilization under various congestion situations.

PIE: A lightweight control scheme to address the bufferbloat problem

A system includes an on-board unit (OBU) in communication with an internal subsystem in a vehicle on at least one Ethernet network and a node on a wireless network. A method in one embodiment includes receiving a message on the Ethernet network in the vehicle, encapsulating the message to facilitate translation to Ethernet protocol if the message is not in Ethernet protocol, and transmitting the message in Ethernet protocol to its destination. Certain embodiments include optimizing data transmission over the wireless network using redundancy caches, dictionaries, object contexts databases, speech templates and protocol header templates, and cross layer optimization of data flow from a receiver to a sender over a TCP connection. Certain embodiments also include dynamically identifying and selecting an operating frequency with least interference for data transmission over the wireless network.

System and method for internal networking, data optimization and dynamic frequency selection in a vehicular environment

Bufferbloat is a phenomenon in which excess buffers in the network
cause high latency and latency variation. As more and more interactive
applications (e.g., voice over IP, real-time video streaming, and
financial transactions) run in the Internet, high latency and latency
variation degrade application performance. There is a pressing need to
design intelligent queue management schemes that can control latency
and latency variation, and hence provide desirable quality of service
to users. This document presents a lightweight active queue
management design called "PIE" (Proportional Integral controller
Enhanced) that can effectively control the average queuing latency to
a target value. Simulation results, theoretical analysis, and Linux
testbed results have shown that PIE can ensure low latency and achieve
high link utilization under various congestion situations. The design
does not require per-packet timestamps, so it incurs very little
overhead and is simple enough to implement in both hardware and
software.

Proportional Integral Controller Enhanced (PIE): A Lightweight Control Scheme to Address the Bufferbloat Problem

A method in one embodiment includes intercepting a message in an on-board unit (OBU) of a vehicular network environment between a source and a receiver in the vehicular network environment, verifying the message is sent from the source, verifying the message is not altered, evaluating a set of source flow control policies associated with the source, and blocking the message if the set of source flow control policies indicate the message is not permitted. In specific embodiments, the message is not permitted if a level of access assigned to the source in the set of source flow control policies does not match a level of access tagged on the message. In further embodiments, the method includes evaluating a set of receiver flow control policies associated with the receiver, and blocking the message if the set of receiver flow control policies indicates the message is not permitted.

System and method for wireless interface selection and for communication and access control of subsystems, devices, and data in a vehicular environment

Rong Pan

Papers

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

PIE: A lightweight control scheme to address the bufferbloat problem

System and method for internal networking, data optimization and dynamic frequency selection in a vehicular environment

Proportional Integral Controller Enhanced (PIE): A Lightweight Control Scheme to Address the Bufferbloat Problem

System and method for wireless interface selection and for communication and access control of subsystems, devices, and data in a vehicular environment