Due to widespread popularity of streaming services, many streaming clients typically compete over bottleneck links for their own bandwidth share. However, in such environments, the rate adaptation algorithms used by modern streaming clients often result in instability and unfairness, which negatively affects the playback experience. In addition, mobile clients often waste bandwidth by trying to stream excessively high video bitrates. We present and evaluate a cap-based framework in which the network and clients cooperate to improve the overall Quality of Experience (QoE). First, to motivate the framework, we conduct a comprehensive study using the lab setup showing that a fixed rate cap comes with both benefits (e.g., data savings, improved stability and fairness) and drawbacks (e.g., higher startup times and slower recovery after stalls). To address the drawbacks while keeping the benefits, we then introduce and evaluate a framework that includes (i) buffer-aware rate caps in which the network temporarily boosts the rate cap of clients during video startup and under low buffer conditions, and (ii) boost-aware client-side adaptation algorithms that optimize the bitrate selection during the boost periods. Combined with information sharing between the network and clients, these mechanisms are shown to improve QoE, while reducing wasted bandwidth.

With 360° video, only a limited fraction of the full view is displayed at each point in time. This has prompted the design of streaming delivery techniques that allow alternative playback qualities to be delivered for each candidate viewing direction. However, while prefetching based on the user's expected viewing direction is best done close to playback deadlines, large buffers are needed to protect against shortfalls in future available bandwidth. This results in conflicting goals and an important prefetch aggressiveness tradeoff problem regarding how far ahead in time from the current play-point prefetching should be done. This paper presents the first characterization of this tradeoff. The main contributions include an empirical characterization of head movement behavior based on data from viewing sessions of four different categories of 360° video, an optimization-based comparison of the prefetch aggressiveness tradeoffs seen for these video categories, and a data-driven discussion of further optimizations, which include a novel system design that allows both tradeoff objectives to be targeted simultaneously. By qualitatively and quantitatively analyzing the above tradeoffs, we provide insights into how to best design tomorrow's delivery systems for 360° videos, allowing content providers to reduce bandwidth costs and improve users' playback experiences.

The postal system is often used as an analogy when describing Internet routing. However, in addition to similarities, there are some significant differences. First, and most importantly, the Autonomous Systems (ASes) that operate the routers along the end-to-end path of a packet can often inspect and manipulate the packet and its content. Second, due to lack of secure routing mechanisms, packet paths can be diverted through additional non-trusted ASes. Although we often know the first network we connect through and the service that we access, we seldom know the networks that forward our packets. We can think of these networks as hidden mailmen. To better understand these networks and their potential access to information, we characterize the ASes along the paths of typical Internet packets between European example clients and the most popular web domains. We also identify ASes and countries with higher path coverage and investigate if there are differences in the HTTPS usage among paths that may take additional detours. Our results highlight the role played by North American (typically US-based) ASes and glean insights into how vulnerable the detoured traffic is to man-in-the-middle attacks compared to regular traffic.

Stalls during video playback are perhaps the most important indicator of a client's viewing experience. To provide the best possible service, a proactive network operator may therefore want to know the buffer conditions of streaming clients and use this information to help avoid stalls due to empty buffers. However, estimation of clients' buffer conditions is complicated by most streaming services being rate-adaptive, and many of them also encrypted. Rate adaptation reduces the correlation between network throughput and client buffer conditions. Usage of HTTPS prevents operators from observing information related to video chunk requests, such as indications of rate adaptation or other HTTP-level information.

In contrast to traditional video, multi-view video streaming allows viewers to interactively switch among multiple perspectives provided by different cameras. One approach to achieve such a service is to encode the video from all of the cameras into a single stream, but this has the disadvantage that only a portion of the received video data will be used, namely that required for the selected view at each point in time. In this paper, we introduce the concept of a “multi-video stream bundle” that consists of multiple parallel video streams that are synchronized in time, each providing the video from a different camera capturing the same event or movie. For delivery we leverage the adaptive features and time-based chunking of HTTP-based adaptive streaming, but now employing adaptation in both content and rate. Users are able to change their viewpoint on-demand and the client player adapts the rate at which data are retrieved from each stream based on the user's current view, the probabilities of switching to other views, and the user's current bandwidth conditions. A crucial component of such a system is the prefetching policy. For this we present an optimization model as well as a simpler heuristic that can balance the playback quality and the probability of playback interruptions. After analytically and numerically characterizing the optimal solution, we present a prototype implementation and sample results. Our prefetching and buffer management solution is shown to provide close to seamless playback switching when there is sufficient bandwidth to prefetch the parallel streams.

This paper considers the problem of providing users playing one streaming video the option of instantaneous and seamless playback of alternative videos. Recommendation systems can easily provide a list of alternative videos, but there is little research on how to best eliminate the startup time for these alternative videos. The problem is motivated by services that want to retain increasingly impatient users, who frequently watch the beginning of multiple videos, before viewing a video to the end. We present the design, implementation, and evaluation of an HTTP-based Adaptive Streaming (HAS) solution that provides careful prefetching and buffer management. We also present the design and evaluation of three fundamental policy classes that provide different tradeoffs between how aggressively new alternative videos are prefetched versus the importance of ensuring high playback quality. We show that our solution allows us to reduce the startup times of alternative videos by an order of magnitude and effectively adapt the quality such as to ensure the highest possible playback quality of the video being viewed. By improving the channel utilization we also address the discrimination problem that HAS clients often suffer from, allowing us to in some cases simultaneously improve the playback quality of the video being viewed and provide the value-added service of allowing instantaneous playback of the prefetched alternative videos.

Software Defined Networking (SDN) is a new networking paradigm that permits to slice network infrastructures. An example of SDN is the OpenFlow framework, where the control plane runs on a separate device, called controller, that manages data forwarding switches. The OpenFlow protocol ensures communications between OpenFlow switches and the OpenFlow controller. Before widely deploying OpenFlow based networks, scalability and performance of such networks should be studied and better understood. In this paper, the scalability of NOX, one of the most popular OpenFlow controller, is analyzed through both simulation and lab measurements. We perform an Ethernet trace analysis on the controller by defining flow characteristics as would be seen by an OpenFlow controller. We study the potential trace impact on an OpenFlow controller, analyzing among others, the number of flows, flow inter arrival times, traffic volumes and flow size distribution. Our results permit to discuss the feasibility of running OpenFlow networks with a single commodity PC as the controller in a mid-size campus network.

Interactive branched video that allows users to select their own paths through the video, provides creative content designers with great personalization opportunities; however, such video also introduces significant new challenges for the system developer. For example, without careful prefetching and buffer management, the use of multiple alternative playback paths can easily result in playback interruptions. In this paper, we present a full implementation of an interactive branched video player using HTTP-based Adaptive Streaming (HAS) that provides seamless playback even when the users defer their branch path choices to the last possible moment. Our design includes optimized prefetching policies that we derive under a simple optimization framework, effective buffer management of prefetched data, and the use of parallel TCP connections to achieve efficient buffer workahead. Through performance evaluation under a wide range of scenarios, we show that our optimized policies can effectively prefetch data of carefully selected qualities along multiple alternative paths such as to ensure seamless playback, offering users a pleasant viewing experience without playback interruptions.

HTTP-based Adaptive Streaming (HAS) has become a widely-used video delivery technology. Use of HTTP enables relatively easy firewall/NAT traversal and content caching. While caching is an important aspect of HAS, there is not much public research on the performance impact proxies and their policies have on HAS. In this paper we build an experimental framework using open source Squid proxies and the most recent Open Source Media Framework (OSMF). A range of content-aware policies can be implemented in the proxies and tested, while the player software can be instrumented to measure performance as seen at the client. Using this framework, the paper makes three main contributions. First, we present a scenario-based performance evaluation of the latest version of the OSMF player. Second, we quantify the benefits using different proxy-assisted solutions, including basic best effort policies and more advanced content quality aware prefetching policies. Finally, we present and evaluate a cooperative framework in which clients and proxies share information to improve performance. In general, the bottleneck location and network conditions play central roles in which policy choices are most advantageous, as they significantly impact the relative performance differences between policy classes. We conclude that careful design and policy selection is important when trying to enhance HAS performance using proxy assistance.