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In this paper we present the key collaboration challenges and recommendations identified by targeted research communities during the Eastern Regional Network (ERN) [7] Architecture and Federation Virtual Workshop[6], for validation of the base design of the ERN Federated OpenCI Labs collaborative infrastructure model. The workshop was designed to stimulate open discussions surrounding key aspects of collaborative scientific research and workflows. A brief summary of the key data gathered is provided here. The findings from this workshop have led to a re-evaluation of the design of ERN Federated OpenCI Labs infrastructure.

We demonstrate a video 360 navigation and streaming system for Mobile HMD devices. The Navigation Graph (NG) concept is used to predict future views that use a graph model that captures both temporal and spatial viewing behavior of prior viewers. Visualization of video 360 content navigation and view prediction algorithms is used for assessment of Quality of Experience (QoE) and evaluation of the accuracy of the NG-based view prediction algorithm.

While adaptive bitrate (ABR) streaming has contributed significantly to the reduction of video playout stalling, ABR clients continue to suffer from the variation of bit rate qualities over the duration of a streaming session. Similar to stalling, these variations in bit rate quality have a negative impact on the users' Quality of Experience (QoE). In this paper, we use a trace from a large-scale CDN to show that such quality changes occur in a significant amount of streaming sessions and investigate an ABR video segment retransmission approach to reduce the number of such quality changes. As the new HTTP/2 standard is becoming increasingly popular, we also see an increase in the usage of QUIC as an alternative protocol for the transmission of web traffic including video streaming. Using various network conditions, we conduct a systematic comparison of existing transport layer approaches for HTTP/2 that is best suited for ABR segment retransmissions. Since it is well known that both protocols provide a series of improvements over HTTP/1.1, we perform experiments both in controlled environments and over transcontinental links in the Internet and find that these benefits also "trickle up'' into the application layer when it comes to ABR video streaming where QUIC retransmissions can significantly improve the average quality bitrate while simultaneously minimizing bit rate variations over the duration of a streaming session.

Despite known QoE shortcomings, Dynamic Adaptive Streaming over HTTP (DASH) has been tied with TCP for many years now. The advent of HTTP/2 powered by transport protocols such as QUIC provides an excellent opportunity to revisit adaptive bitrate streaming with respect to QoE. QUIC promises improved congestion control, zero-RTT connection establishment and multiplexing logical streams. In this work, we adapt state-of-the-art DASH players with buffer-based and hybrid (rate/buffer-based) quality adaptation logic to use QUIC. Our main focus lies in contrasting the QoE performance of DASH algorithms running on top of QUIC versus TCP in various environments. Interestingly, we find through testbed and Internet measurements that QUIC does not provide a boost to current DASH algorithms but instead a degradation in the chosen quality bitrates.

With the rising importance of video streaming in the Internet, dynamic adaptive streaming over HTTP (DASH) has been established as a key technology for video delivery. Yet, variable network conditions often result in a limited quality of experience (QoE)—with the interrelation of cross-layer network factors and DASH mechanisms widely unexplored. To understand the complex dependencies between DASH configurations and network conditions, we propose a systematic extensive large-scale emulation approach with state-of- the-art QoE metrics. Using this approach with a real DASH player in Mininet, we emulated more than 10, 000 combinations of static and dynamic network conditions and DASH configurations to derive their QoE. The obtained results show that no single DASH configuration provides the highest achievable QoE. Depending on the network conditions, specific combinations of the TCP congestion control, segment sizes and the DASH adaptation algorithm provide higher QoE—showing the possibility of performance improvements in practice. Furthermore, the ex- tensive emulations show a linear relation between delay, loss and QoE that is mostly independent of bandwidth.

The application-layer based control loops of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection a complex problem. In this work, we review and present new insights into the challenges of DASH rate adaptation. We identify several critical issues that contribute to the degradation of DASH performance with respect to the rate control loops of DASH and TCP. We then introduce a novel DASH quality adaptation algorithm SQUAD, which is specifically designed to ensure high quality of experience (QoE). We implement and test our algorithm together with a number of state-of-the-art quality adaptation algorithms. Through extensive experiments on both testbed and cross-Atlantic Internet scenarios, we show that by sacrificing little to none in average quality bitrate, SQUAD provides significantly better QoE in terms of number and magnitude of quality switches.

This paper studies information dissemination in wireless ad hoc networks, using standard routing protocols, such as OLSR, as well as Information-Centric Networking. We performed simulations using NS-3 and ndnSIM with different node counts and transport protocols. Our simulations show that TCP performs better in lower hop count scenarios, while NDN performs better in higher hop count scenarios.

The current approach to transcoding in adaptive bit rate streaming is to transcode all videos in all possible bit rates which wastes transcoding resources and storage space, since a large fraction of the transcoded video segments are never watched by users. To reduce transcoding work, we propose several online transcoding policies that transcode video segments in a "just-in-time" fashion such that a segment is transcoded only to those bit rates that are actually requested by the user. However, a reduction in the transcoding work should not come at the expense of a significant reduction in the quality of experience of the users. To establish the feasibility of online transcoding, we first show that the bit rate of the next video segment requested by a user can be predicted ahead of time with an accuracy of 99.7% using a Markov prediction model. This allows our online algorithms to complete transcoding the required segment ahead of when it is needed by the user, thus reducing the possibility of freezes in the video playback. To derive our results, we collect and analyze a large amount of request traces from one of the world's largest video CDNs consisting of over 200 thousand unique users watching 5 million videos over a period of three days. The main conclusion of our work is that online transcoding schemes can reduce transcoding resources by over 95% without a major impact on the users' quality of experience.

As shown in recent studies, video streaming is by far the biggest category of backbone Internet traffic in the US. As a measure to reduce the cost of highly over-provisioned physical infrastructures while remaining the quality of video services, many streaming service providers started to use cloud services where physical resources can be dynamically allocated based on current demand. This paper characterizes the performance of Dynamic Adaptive Streaming over HTTP (DASH), a new MPEG standard on adaptive streaming, in the cloud. We seek to answer the following questions that are critical to content providers that are hosting video in clouds: Which data center is the best to host videos? Does geographical distance matter? What type of instance is best suitable depending on different needs? How to efficiently solve the trade-off between performance and cost? The measurement methods and results presented in this paper can be easily expanded into other VoD services, and they allow us to i) characterize DASH behavior when streaming from the cloud; ii) identify the key factors that influence the DASH performance; and iii) suggest improvements for related services.

The growth of smartphones combined with advances in mobile networking have revolutionized the way people consume multimedia data. In particular, users in developing countries primarily rely on smartphones since they often do not have access to more powerful (and more expensive) computing devices. Unfortunately, cellular networks in developing countries have historically had low reliability, due to grid instability and lack of infrastructure. The situation has led network operators to experiment with running cellular towers "off the grid" using intermittent renewable energy sources. In parallel, network operators are also experimenting with co-locating server caches close to cell towers to reduce access latency and back-haul bandwidth. In this paper, we study techniques for optimizing multimedia caches for intermittent renewable energy sources. Specifically, we examine how to apply a blinking abstraction proposed in prior work, which rapidly transitions servers between an active and inactive state, to improve the performance of a multimedia cache powered by renewables, called GreenCache. Our results show that GreenCache's staggered load-proportional blinking policy, which coordinates when servers are active over brief intervals, results in 3X less buffering (or pause) time by the client compared to an activation blinking policy, which simply activates and deactivates servers over long periods as power fluctuates, for realistic power variations from renewable energy sources.

Following advice from the YouTube recommendation system is one of the ways users browse through the videos offered by YouTube. The system presents related videos based on several factors depending on the current video requested. This related videos list can be used by caching infrastructure to reduce network bandwidth consumption. In this paper, we analyze the differences between user-specific recommendation lists. We perform this analysis on 100s of user nodes from all around the world divided into 4 geographical regions using PlanetLab. Based on our analysis, we find that the related videos differ less in the top half (1-10) of the related video list offered by YouTube compared to the bottom half (11-20). Based on our analysis, we suggest that, caching or prefetching of the Top 10 of the related videos is advantageous over a period of time than caching the whole list offered by YouTube.

Steerable sensors, such as pan-tilt-zoom video cameras, expose programmable actuators to applications, which steer them in different directions based on their goals. Despite being expensive to deploy and maintain, existing steerable sensor networks allow only a single application to control them due to the slow speed of their mechanical actuators. To address the problem, we design MultiSense to enable fine-grained multiplexing by (i) exposing a virtual sensor to each application and (ii) optimizing the time to context-switch between virtual sensors and satisfy requests.We implement MultiSense in Xen and explore how well proportional share scheduling, along with extensions for state restoration and request batching, satisfies the unique requirements of steerable sensors in the form of pan-tilt-zoom video cameras. We present experiments that show MultiSense efficiently isolates the performance of virtual cameras, allowing concurrent applications to satisfy conflicting goals. As one example, we enable a tracking application to photograph an object moving at nearly 3 mph every 23 ft along its trajectory at a distance of 300 ft, while supporting a security application that photographs a fixed point every 3 seconds

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by showing that video playbacks of videos of YouTube is often unsatisfactory and introduce a series of prefetching schemes: the conventional caching scheme, the search result-based prefetching scheme, and the recommendation-aware prefetching scheme. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates a strong potential for improving the playback quality at the client. We also explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

In this paper, we focus on a network of rich sensors that are geographically distributed and argue that the design of such networks poses very different challenges from traditional "mote-class" sensor network design. We identify the need to handle the diverse requirements of multiple users to be a major design challenge, and propose a utility-driven approach to maximize data sharing across users while judiciously using limited network and computational resources. Our utility-driven architecture addresses three key challenges for such rich multi-user sensor networks: how to define utility functions for networks with data sharing among end-users, how to compress and prioritize data transmissions according to its importance to end-users, and how to gracefully degrade end-user utility in the presence of bandwidth fluctuations. We instantiate this architecture in the context of geographically distributed wireless radar sensor networks for weather, and present results from an implementation of our system on a multi-hop wireless mesh network that uses real radar data with real end-user applications. Our results demonstrate that our progressive compression and transmission approach achieves an order of magnitude improvement in application utility over existing utility-agnostic non-progressive approaches, while also scaling better with the number of nodes in the network.

The concept of authenticating users e.g. by means of a login process is very well established and there is no doubt that it is absolutely necessary and helpful in a multiuser environment. Unfortunately specific information about a user originating a data stream or receiving it, is often no longer available at the traversed network nodes. This applies to the even more specific question of what application is used as well. Routers, gateways or firewalls usually have to base their classification of data on IP header inspection or have to try to extract information from the packets payload.We present an approach that works transparently and allows to associate user and application specific information with IP data streams by only slightly modifying components of the operating system environment and infrastructure components. On top of this framework we show usage scenarios for dedicatedly placing copyright information in media content and for an enhancement of the interoperation with the security infrastructure.