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The demand and usage of 360°video services are expected to increase. However, despite these services being highly bandwidth intensive, not much is known about the potential value that basic bandwidth saving techniques such as server or edge-network on-demand caching (e.g., in a CDN) could have when used for delivery of such services. This problem is both important and complicated as client-side solutions have been developed that split the full 360°view into multiple tiles, and adapt the quality of the downloaded tiles based on the user’s expected viewing direction and bandwidth conditions. This paper presents new trace-based analysis methods that incorporate users’ viewports (the area of the full 360°view the user actually sees), a first characterization of the cross-user similarities of the users’ viewports, and a trace-based analysis of the potential bandwidth savings that caching-based techniques may offer under different conditions. Our analysis takes into account differences in the time granularity over which viewport overlaps can be beneficial for resource saving techniques, compares and contrasts differences between video categories, and accounts for uncertainties in the network conditions and the prediction of the future viewing direction when prefetching. The results provide substantial insight into the conditions under which overlap can be considerable and caching effective, and inform the design of new caching system policies tailored for 360°video., Funding: Swedish Research Council (VR)

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 users current view, the probabilities of switching to other views, and the users 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., Funding Agencies|Swedish Research Council (VR); Center for Industrial Information Technology (CENIIT); Natural Sciences and Engineering Research Council of Canada

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 users current view, the probabilities of switching to other views, and the users 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., Funding Agencies|Swedish Research Council (VR); Center for Industrial Information Technology (CENIIT); Natural Sciences and Engineering Research Council of Canada

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on kth request for different k. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect "oracle" policy, such improvements are unlikely to be achievable in practice., Funding Agencies|Center for Industrial Information Technology (CEN-IIT); Natural Sciences and Engineering Research Council (NSERC) of Canada

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on kth request for different k. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect "oracle" policy, such improvements are unlikely to be achievable in practice., Funding Agencies|Center for Industrial Information Technology (CEN-IIT); Natural Sciences and Engineering Research Council (NSERC) of Canada

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 users current view, the probabilities of switching to other views, and the users 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., Funding Agencies|Swedish Research Council (VR); Center for Industrial Information Technology (CENIIT); Natural Sciences and Engineering Research Council of Canada

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on kth request for different k. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect "oracle" policy, such improvements are unlikely to be achievable in practice., Funding Agencies|Center for Industrial Information Technology (CEN-IIT); Natural Sciences and Engineering Research Council (NSERC) of Canada

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on kth request for different k. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect "oracle" policy, such improvements are unlikely to be achievable in practice., Funding Agencies|Center for Industrial Information Technology (CEN-IIT); Natural Sciences and Engineering Research Council (NSERC) of Canada

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on kth request for different k. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect "oracle" policy, such improvements are unlikely to be achievable in practice., Funding Agencies|Center for Industrial Information Technology (CEN-IIT); Natural Sciences and Engineering Research Council (NSERC) of Canada

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 users current view, the probabilities of switching to other views, and the users 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., Funding Agencies|Swedish Research Council (VR); Center for Industrial Information Technology (CENIIT); Natural Sciences and Engineering Research Council of Canada

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 users current view, the probabilities of switching to other views, and the users 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., Funding Agencies|Swedish Research Council (VR); Center for Industrial Information Technology (CENIIT); Natural Sciences and Engineering Research Council of Canada

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.

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.

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.

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.

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.

Geographically distributed cloud platforms enable an attractive approach to large-scale content delivery. Storage at various sites can be dynamically acquired from (and released back to) the cloud provider so as to support content caching, according to the current demands for the content from the different geographic regions. When storage is sufficiently expensive that not all content should be cached at all sites, two issues must be addressed: how should requests for content be routed to the cloud provider sites, and what policy should be used for caching content using the elastic storage resources obtained from the cloud provider. Existing approaches are typically designed for non-elastic storage and little is known about the optimal policies when minimizing the delivery costs for distributed elastic storage. In this paper, we propose an approach in which elastic storage resources are exploited using a simple dynamic caching policy, while request routing is updated periodically according to the solution of an optimization model. Use of pull-based dynamic caching, rather than push-based placement, provides robustness to unpredicted changes in request rates. We show that this robustness is provided at low cost \textendash{} even with fixed request rates, use of the dynamic caching policy typically yields content delivery cost within 10\% of that with the optimal static placement. We compare request routing according to our optimization model to simpler baseline routing policies, and find that the baseline policies can yield greatly increased delivery cost relative to optimized routing. Finally, we present a lower-cost approximate solution algorithm for our routing optimization problem that yields content delivery cost within 2.5\% of the optimal solution.

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms., Nominated as the Best Paper in the 2013 ACM SIGCOMM workshop on Future human-centric Multimedia Networking (FhMN)

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms., Nominated as the Best Paper in the 2013 ACM SIGCOMM workshop on Future human-centric Multimedia Networking (FhMN)

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms., Nominated as the Best Paper in the 2013 ACM SIGCOMM workshop on Future human-centric Multimedia Networking (FhMN)

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms., Nominated as the Best Paper in the 2013 ACM SIGCOMM workshop on Future human-centric Multimedia Networking (FhMN)

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms., Nominated as the Best Paper in the 2013 ACM SIGCOMM workshop on Future human-centric Multimedia Networking (FhMN)

This paper presents new results on characterization and modeling of user-generated video popularity evolution, based on a recent complementary data collection for videos that were previously the subject of an eight month data collection campaign during 2008/09. In particular, during 2011, we collected two contiguous months of weekly view counts for videos in two separate 2008/09 datasets, namely the ``recently-uploaded'' and the ``keyword-search'' datasets. These datasets contain statistics for videos that were uploaded within 7 days of the start of data collection in 2008 and videos that were discovered using a keyword search algorithm in 2008, respectively. Our analysis shows that the average weekly view count for the recently-uploaded videos had not decreased by the time of the second measurement period, in comparison to the middle and later portions of the first measurement period. The new data is used to evaluate the accuracy of a previously proposed model for synthetic view count generation for time periods that are substantially longer than previously considered. We find that the model yielded distributions of total (lifetime) video view counts that match the empirical distributions, however, significant differences between the model and empirical data were observed with respect to other metrics. These differences appear to arise because of particular popularity characteristics that change over time rather than being week-invariant as assumed in the model.

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms.

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms., Nominated as the Best Paper in the 2013 ACM SIGCOMM workshop on Future human-centric Multimedia Networking (FhMN)

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.

With the proliferation of cloud services, cloud-based systems can become a cost-effective means of on-line content delivery. In order to make best use of the available cloud bandwidth and storage resources, content distributors need to have a good understanding of the tradeoffs between various system design choices. In this work we consider a peer-assisted content delivery system that aims to provide guaranteed average download rate to its customers. We show that bandwidth demand peaks for contents with moderate popularity, and identify these contents as candidates for cloud-based service. We then consider dynamic content bundling and cross-swarm seeding, which were recently proposed to improve download performance, and evaluate their impact on the optimal choice of cloud service use. We find that much of the benefits from peer seeding can be achieved with careful torrent inflation, and that hybrid policies that combine bundling and peer seeding often reduce the delivery costs by 20% relative to only using seeding. Furthermore, all these peer-assisted policies reduce the number of files that would need to be pushed to the cloud. Finally, we show that careful system design is needed if locality is an important criterion when choosing cloud-based service provisioning.

One highly-scalable approach to content delivery is to harness the upload bandwidth of the clients. Peer-assisted content delivery systems have been shown to effectively offload the servers of popular files, as the request rates of popular content enable the formation of self-sustaining torrents, where the entire content of the file is available among the peers themselves. However, for less popular files, these systems are less helpful in offloading servers. With a long tail of mildly popular content, with a high aggregate demand, a large fraction of the file requests must still be handled by servers. In this paper, we present the design, implementation, and evaluation of a dynamic file bundling system, where peers are requested to download content which they may not otherwise download in order to "inflate" the popularity of less popular files. Our system introduces the idea of a super bundle, which consists of a large catalogue of files. From this catalogue, smaller bundles, consisting of a small set of files, can dynamically be assigned to individual users. The system can dynamically adjust the number of downloaders of each file and thus enables the popularity inflation to be optimized according to current file popularities and the desired tradeoff between download times and server resource usage. The system is evaluated on PlanetLab.

Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity. In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.

Peer-assisted content delivery systems can provide scalable download service for popular files. For mildly popular content, however, these systems are less helpful in offloading servers as the request rate for less popular files may not enable formation of self-sustaining torrents (where the entire content of the file is available among the peers themselves). As there typically is a long tail of mildly popular content, with a high aggregate demand, a large fraction of the file requests must still be handled by servers, and is not off-loadable to peers. Bundling approaches have been proposed where peers are requested to download content which they may not otherwise be interested in order to ``inflate'' the popularity of less popular files. We present the design and implementation of a dynamic bundling system, in which a large number of files may be bundled to form a super bundle. From this super bundle, smaller individual bundles, consisting of a small set of files, can dynamically be assigned to individual users. Our system has the capability to dynamically adjust the number of downloaders of each file, thus allowing popularity inflation to be optimized according to current file popularities.

A peer-assisted content delivery system uses the upload bandwidth of its clients to assist in delivery of popular content. In peer-assisted systems using a BitTorrent-like protocol, a content delivery server seeds the offered files, and active torrents form when multiple clients make closely-spaced requests for the same content. Scalability is achieved in the sense of being able to accommodate arbitrarily high request rates for individual files. Scalability with respect to the number of files, however, may be much more difficult to achieve, owing to a “long tail” of lukewarm or cold files for which the server may need to assume most or all of the delivery cost. This paper first addresses the question of how best to allocate server resources among multiple active torrents. We then propose new content delivery policies that use some of the available upload bandwidth from currently downloading clients to “inflate” torrents for files that would otherwise require substantial server bandwidth. Our performance results show that use of torrent inflation can substantially reduce download times, by more than 50% in some cases.

Systems delivering stored video content using a peer-assisted approach are able to serve large numbers of concurrent requests by utilizing upload bandwidth from their clients to assist in delivery. In systems providing download service, BitTorrent-like protocols may be used in which “tit-for-tat” policies provide incentive for clients to contribute upload bandwidth. For on-demand streaming delivery, however, in which clients begin playback well before download is complete, all prior proposed protocols rely on peers at later video play points uploading data to peers at earlier play points that do not have data to share in return. This paper considers the problem of devising peer-assisted protocols for streaming systems that, similar to download systems, provide effective “tit-for-tat” incentives for clients to contribute upload bandwidth. We propose policies that provide such incentives, while also providing short start-up delays, and delivery of (almost) all video frames by their respective playback deadlines.

Content delivery providers can improve their service scalability and offload their servers by making use of content transfers among their clients. To provide peers with incentive to transfer data to other peers, protocols such as BitTorrent typically employ a tit-for-tat policy in which peers give upload preference to peers that provide the highest upload rate to them. However, the tit-for-tat policy does not provide any incentive for a peer to stay in the system beyond completion of its download. This paper presents a simple fixed-point analytic model of a priority-based incentive mechanism which provides peers with strong incentive to contribute upload bandwidth beyond their own download completion. Priority is obtained based on a peer's prior contribution to the system. Using a two-class model, we show that priority-based policies can significantly improve average download times, and that there exists a significant region of the parameter space in which both high-priority and low-priority peers experience improved performance compared to with the pure tit-for-tat approach. Our results are supported using event-based simulations.

With BitTorrent-like protocols a client may download a file from a large and changing set of peers, using connections of heterogeneous and time-varying bandwidths. This flexibility is achieved by breaking the file into many small pieces, each of which may be downloaded from different peers. This paper considers an approach to peer-assisted on-demand delivery of stored media that is based on the relatively simple and flexible BitTorrent-like approach, but which is able to achieve a form of “streaming” delivery, in the sense that playback can begin well before the entire media file is received. Achieving this goal requires: (1) a piece selection strategy that effectively mediates the conflict between the goals of high piece diversity, and the in-order requirements of media file playback, and (2) an on-line rule for deciding when playback can safely commence. We present and evaluate using simulation candidate protocols including both of these components.