The Internet has evolved greatly from its original incarnation. For instance, the vast majority of current Internet usage is data retrieval and service access, whereas the architecture was designed around host-to-host applications such as telnet and ftp. Moreover, the original Internet was a purely transparent carrier of packets, but now the various network stakeholders use middleboxes to improve security and accelerate applications. To adapt to these changes, we propose the Data-Oriented Network Architecture (DONA), which involves a clean-slate redesign of Internet naming and name resolution.

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A data-oriented (and beyond) network architecture

This paper presents DONet, a data-driven overlay network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement, as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient, as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient member and partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents. We have extensively evaluated the performance of DONet over the PlanetLab. Our experiments, involving almost all the active PlanetLab nodes, demonstrate that DONet achieves quite good streaming quality even under formidable network conditions. Moreover, its control overhead and transmission delay are both kept at low levels. An Internet-based DONet implementation, called CoolStreaming v.0.9, was released on May 30, 2004, which has attracted over 30000 distinct users with more than 4000 simultaneously being online at some peak times. We discuss the key issues toward designing CoolStreaming in this paper, and present several interesting observations from these large-scale tests; in particular, the larger the overlay size, the better the streaming quality it can deliver.

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CoolStreaming/DONet: a data-driven overlay network for peer-to-peer live media streaming

In the last decade, the research topic of automatic analysis of facial expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial expression analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial expression database difficult. We then present the MMI facial expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial expression analysis to date.

https://ibug.doc.ic.ac.uk/media/uploads/documents/PanticEtAl-ICME2005-final.pdf

Web-based database for facial expression analysis

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

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

As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.

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Understanding the impact of video quality on user engagement

While application end-point architectures have proven to be viable solutions for large-scale distributed applications such as distributed computing and file-sharing, there is little known about its feasibility for more bandwidth-demanding applications such as live streaming. Heterogeneity in bandwidth resources and dynamic group membership, inherent properties of application end-points, may adversely affect the construction of a usable and efficient overlay. At large scales, the problems become even more challenging. In this paper, we study one of the most prominent architectural issues in overlay multicast: the feasibility of supporting large-scale groups using an application end-point architecture. We look at three key requirements for feasibility: (i) are there enough resources to construct an overlay, (ii) can a stable and connected overlay be maintained in the presence of group dynamics, and (iii) can an efficient overlay be constructed? Using traces from a large content delivery network, we characterize the behavior of users watching live audio and video streams. We show that in many common real-world scenarios, all three requirements are satisfied. In addition, we evaluate the performance of several design alternatives and show that simple algorithms have the potential to meet these requirements in practice. Overall, our results argue for the feasibility of supporting large-scale live streaming using an application end-point architecture.

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The feasibility of supporting large-scale live streaming applications with dynamic application end-points

In this paper, we report on experience in building and deploying an operational Internet broadcast system based on Overlay Multicast. In over a year, the system has been providing a cost-effective alternative for Internet broadcast, used by over 4000 users spread across multiple continents in home, academic and commercial environments. Technical conferences and special interest groups are the early adopters. Our experience confirms that Overlay Multicast can be easily deployed and can provide reasonably good application performance. The experience has led us to identify first-order issues that are guiding our future efforts and are of importance to any Overlay Multicast protocol or system. Our key contributions are (i) enabling a real Overlay Multicast application and strengthening the case for overlays as a viable architecture for enabling group communication applications on the Internet, (ii) the details in engineering and operating a fully functional streaming system, addressing a wide range of real-world issues that are not typically considered in protocol design studies, and (iii) the data, analysis methodology, and experience that we are able to report given our unique standpoint.

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Early experience with an internet broadcast system based on overlay multicast

As Internet video goes mainstream, we see increasing user expectations for higher video quality and new global policy requirements for content providers. Inspired by the case for centralizing network-layer control, we present C3, a control system for optimizing Internet video delivery. The design of C3 addresses key challenges in ensuring scalability and tackling data plane heterogeneity. First, to ensure scalability and responsiveness, C3 introduces a novel split control plane architecture that can tolerate a small increase in model staleness for a dramatic increase in scalability. Second, C3 supports diverse client-side platforms via a minimal clientside sensing/actuation layer and offloads complex monitoring and control logic to the control plane. C3 has been operational for eight years, and today handles more than 100M sessions per day from 244 countries for 100+ content providers and has improved the video quality significantly. In doing so, C3 serves as a proof point of the viability of fine-grained centralized control for Internetscale applications. Our experiences reinforce the case for centralizing control with the continued emergence of new use case pulls (e.g., client diversity) and technology pushes (e.g., big data platforms).

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Aditya Ravikumar Ganjam

Papers

Understanding the impact of video quality on user engagement

The feasibility of supporting large-scale live streaming applications with dynamic application end-points

Early experience with an internet broadcast system based on overlay multicast

Understanding the impact of video quality on user engagement

C3: internet-scale control plane for video quality optimization