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Given the rapid growth of user-generated videos, internet traffic has been heavily dominated by online video streaming. Caching videos on edge servers in close proximity to users has been an effective approach to reduce the backbone traffic and the request response time, as well as to improve the video quality on the user side. Video popularity, however, can be highly dynamic over time. The cost of cache replacement at edge servers, particularly that related to service interruption during replacement, is not yet well understood. This paper presents a novel lightweight video caching algorithm for edge servers, seeking to optimize the hit rate with real-time decisions and minimized cost. Inspired by recent advances in deep Q-learning, our DQN-based online video caching (DQN-OVC) makes effective use of the rich and readily available information from users and networks. We decompose the Q-value function as a product of the video value function and the action function, which significantly reduces the state space. We instantiate the action function for cost-aware caching decisions with low complexity so that the cached videos can be updated continuously and instantly with dynamic video popularity. We used video traces from Tencent, one of the largest online video providers in China, to evaluate the performance of our DQN-OVC and to compare it with state-of-the-art solutions. The results demonstrate that DQN-OVC significantly outperforms the baseline algorithms in the edge caching context.

Today's multimedia content generation and sharing have been dramatically boosted by the deep penetration of broadband wireless accesses and the much improved processing power of smart mobile terminals. Mobile users can now instantly capture and share short video clips (usually of several seconds) anywhere and anytime, and consume them with convenient touch screen operations. The instant video clip sharing has emerged as a mainstream application; such pioneers as Twitter's Vine, Miaopai, Instagram, and Snapchat have seen great acceptance, particularly by the youth community. In this paper, we present an initial study on instant video clip sharing. Taking Twitter's Vine as a representative, we systematically investigate its distinct mobile interface, service framework, and user watching behaviors, revealing how this mainstream multimedia service type differentiates from its traditional counterparts. Our trace measurement and analysis demonstrate that instant mobile video clips have a much shorter lifespan and highly skewed popularity that quickly decays over time. This is further aggravated by the unique screen scrolling operation for video browsing. As such, the download-and-watch scheduling used by existing platforms can hardly achieve quality user experience and cost efficiency. We closely investigate and model the input user gestures for scrolling, including drag and fling , and analyze the scheduling policy, partitioning it into prefetching scheduling and watch-time download scheduling. We develop effective solutions toward both subproblems as well as their integration with screen scrolling. The superiority of our enhancement is demonstrated by extensive trace-driven evaluation.

Recent years have witnessed an explosion of gamecasting applications, in which game players (or gamers in short) broadcast game playthroughs by their personal devices in real time. Such pioneer platforms, such as YouTube Gaming, Twitch, and Mobcrush, have attracted a massive number of online broadcasters, and each of them can have hundreds or thousands of fellow viewers. The growing number, however, has created significant challenges to the network and end-devices, particularly considering that bandwidth- and battery-limited smartphones or tablets are becoming dominating for both gamers and viewers. Yet the unique touch operations of the mobile interface offer opportunities, too. In this paper, our measurements based on the real traces from gamers and viewers reveal that strong associations exist between the gamers’ touch interactions and the viewers’ gazing patterns. Motivated by this, we present a novel interaction-aware optimization framework to improve the energy utilization and stream quality for mobile gamecasting . Our framework incorporates a touch-assisted prediction module to extract association rules for gazing pattern prediction and a tile-based optimization module to utilize energy on mobile devices efficiently. Trace-driven simulations illustrate the effectiveness of our framework in terms of energy consumption and stream quality. Our user study experiments also demonstrate much improved (3%–13%) quality satisfaction over the state-of-the-art solution with similar network resources.

Recent years have witnessed the rapid growth of new live broadcast services, represented by Twitch.tv and YouTube live events, where videos are crowdsourced from amateur users (e.g., game players), rather than from commercial and professional TV broadcaster or content providers. The viewers also actively contribute to the content through embedded open-chat channels. Such community interactions among viewers, or even between broadcasters and viewers, make content generation highly diversified and engaging, particularly for the young generation. In this context, cross-viewer synchronization is highly desirable; otherwise the viewers with shorter broadcast latency may act as spoilers, significantly affecting the user experience of other viewers. In this paper, we show that the end-to-end delay has a dramatically amplified impact on the broadcast latency for individual viewers. We suggest smart rate adaptation to achieve cross-viewer synchronization, and develop distributed algorithms based on dual decomposition. We further extend our solution to the cloud environment, and present the concept of ShadowCast, which moves broadcasters to the cloud to provide high-quality streams beyond broadcasters’ network bandwidth constraint. Its practicability and effectiveness is demonstrated by our implementation and test bed experiments.

Driven by the advances in personal computing devices and the prevalence of high-speed network accesses, crowdsourced livecast platforms have emerged in recent years, through which numerous broadcasters lively stream their video content to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are highly heterogeneous in terms of the network/system configurations and, therefore, the generated video quality, which calls for massive encoding and transcoding in order to unify the video sources and serve multiple quality versions to viewers with different configurations. On the other hand, with the rapid evolution in the hardware industry, high-performance processors become mainstream in personal computer market. More end devices can easily transcode high-quality videos in realtime. We witness huge computational resource among the massive fellow viewers that could potentially be used for transcoding. In this paper, we propose CrowdTranscoding, a novel framework for crowdsourced livecast systems that offloads the transcoding assignment to the massive viewers. We identify that the key challenges in CrowdTranscoding are to detect qualified stable viewers and to properly assign them to the source channels. We put forward a viewer crowdsourcing transcode scheduler to smartly schedule the workload assignment. Our solution has been evaluated under diverse viewer/channel conditions as well as different parameter settings. The trace-driven simulation confirms the superiority of CrowdTranscoder, while our PlanetLab-based and real world end-viewer experiments show the practical performance of our approach, which also give hint to the further enhancement.

Empowered by today’s rich tools for media generation and distribution, and the convenient Internet access , streaming crowdsourced multimedia content (crowdsourced streaming, in brief) generalizes the single-source streaming paradigm by including massive contributors for a video/data channel. It calls a joint optimization along the path from crowdsourcers , through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the real world, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.

Established in 2005, YouTube has become the most successful Internet website providing a new generation of short video sharing service. Today, YouTube alone consumes as much bandwidth as did the entire Internet in year 2000 . Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering. In this paper, using traces crawled in a 1.5-year span (from February 2007 to September 2008), we present an in-depth and systematic measurement study on the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length, access pattern, to their active life span. The series of datasets also allow us to identify the growth trend of this fast evolving Internet site, which has seldom been explored before. We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching and peer-to-peer distribution schemes to efficiently deliver videos to end users.

The recently emerged user-generated contents (UGC) services, social networking services (SNS), as well as the pervasive wireless mobile network services have formed social media which has drastically changed the content distribution landscape. Today such UGC applications as YouTube allow any user to be a content provider, generating enormous amount of video contents that are quickly and extensively propagated on the Internet through such SNSes as Facebook and Twitter. Unfortunately, the existing UGC sites are facing critical server bottlenecks and the surges created by the social networking users would make the situation even worse. To better understand the challenges and opportunities therein, we investigate users' social behavior and personal preference of online video sharing from both real-trace measurement study on a popular social networking website and a user questionnaire survey. Our data analysis reveals an interesting coexistence of live streaming and storage sharing, and that the users are generally more interested in watching their friend's videos. It further suggests that even though the traffic is significant, most users are willing to share their resources to assist others, implying user collaboration is a rational choice in this context. In this paper, we present Coordinated Live Streaming and Storage Sharing (COOLS), a system for efficient peer-to-peer posting of user-generated videos. Through a novel ID code design that embeds nodes' locations in an overlay, COOLS leverages stable storage users and yet inherently prioritizes living streaming flows. We also present the improvement of the basic overlay design. The evaluation results show that, as compared to other state-of-the-art solutions, COOLS successfully takes advantage of the coexistence of live streaming and storage sharing, providing better scalability, robustness, and streaming quality.

Much research has focused on caching adaptive videos to improve system performance for heterogeneous clients with diverse access bandwidths. However, existing rate-adaptive caching systems, which are based on layered coding or transcoding, often suffer from a coarse adaptation and/or a high computation overhead. In this paper, we propose an innovative rate-adaptive caching framework that enables low-cost and fine-grained adaptation by using MPEG-4 fine-grained scalable videos. The proposed framework is both network-aware and media-adaptive; i.e., the clients can be of heterogeneous streaming rates, and the backbone bandwidth consumption can be adaptively controlled. We develop efficient cache management schemes to determine the best contents to cache and the optimal streaming rate to each client under the framework. We demonstrate via simulations that, compared to nonadaptive caching, the proposed framework with the optimal cache management not only achieves a significant reduction in the data transmission cost, but also enables a flexible utility assignment for the heterogeneous clients. Our results also show that the framework maintains a low computational overhead, which implies that it is practically deployable

Information security has been a critical issue in the design and development of reliable distributed communication systems and has attracted significant research efforts. A challenging task is how to maintain information security at a high level for multiple-destination video applications with the huge volume of data and dynamic property of clients. This paper proposes a novel Content-Aware Secure Multicast (CASM) protocol for video distribution that seamlessly integrates three important modules: 1)a scalable light-weight algorithm for group key management; 2) a content-aware key embedding algorithm that can make video quality distortion imperceptible and is reliable for clients to detect embedded keys; and 3) a smart two-level video encryption algorithm that can selectively encrypt a small set of video data only, and yet ensure the video as well as the embedded keys unrecognizable without a genuine key. The implementation of the CASM protocol is independent of the underlying multicast mechanism and is fully compatible with existing coding standards. Performance evaluation studies built upon a CASM prototype have demonstrated that CASM is highly robust and scalable in dynamic multicast environments. Moreover, it ensures secure distribution of key and video data with minimized communication and computation overheads. The proposed content-aware key embedding and encryption algorithms are fast enough to support real-time video multicasting.

Video simulcasting enables a sender to generate replicated streams of different rates, serving receivers of diverse access bandwidths. As replication introduces noticeable redundancy, balancing bandwidth consumption with user satisfaction becomes a critical concern in simulcasting. This paper investigates the above issue; more explicitly, we seek answers to the following two questions: what is the number of streams that should be generated, and what is the bandwidth that should be allocated to each stream? We derive optimal and efficient solutions, and evaluate their performance under a variety of configurations. The results demonstrate that an optimal and adaptive bandwidth allocation significantly improves user satisfaction under stringent resource constraints, and an optimal choice of the stream number yields further improvements.

Layered transmission is a promising solution to video multicast over the heterogeneous Internet. However, since the number of layers is practically limited, noticeable mismatches would occur between the coarse-grained layer subscription levels and the heterogeneous and dynamic rate requirements from the receivers. In this paper, we show that such mismatch can be effectively reduced using a dynamic and fine-grained layer rate allocation on the sender's side. Specifically, we study the optimization criteria for rate allocation, and propose a metric called application-aware fairness index. This metric takes into consideration 1) the nonlinear relation between the perceived video quality and the delivered rate and 2) the degree of satisfaction for receivers with heterogeneous bandwidth requirements. We formulate the rate allocation into an optimization problem with the objective of maximizing the expected fairness index for all receivers in a multicast session. We then derive an efficient and scalable solution, and demonstrate that it can be seamlessly integrated into an end-to-end adaptation protocol, called hybrid adaptation layered multicast (HALM). This protocol takes advantage of the emerging fine-grained layered coding, and is fully compatible with the best-effort Internet infrastructure. Simulation and numerical results show that HALM noticeably improves the degree of fairness, and interacts with TCP traffic better than static allocation based protocols. More important, increasing the number of layers in HALM generally improves the degree of fairness; it is sufficient to obtain satisfactory performance with a small number of layers (three to five layers).