vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing

As an important application in the busy world today, mobile video conferencing facilitates virtual face-to-face communication with friends, families and colleagues, via their mobile devices on the move. However, how to provision high-quality, multi-party video conferencing experiences over mobile devices is still an open challenge. The fundamental reason behind is the lack of computation and communication capacities on the mobile devices, to scale to large conferencing sessions. In this paper, we present vSkyConf, a cloud-assisted mobile video conferencing system to fundamentally improve the quality and scale of multi-party mobile video conferencing. By novelly employing a surrogate virtual machine in the cloud for each mobile user, we allow fully scalable communication among the conference participants via their surrogates, rather than directly. The surrogates exchange conferencing streams among each other, transcode the streams to the most appropriate bit rates, and buffer the streams for the most efficient delivery to the mobile recipients. A fully decentralized, optimal algorithm is designed to decide the best paths of streams and the most suitable surrogates for video transcoding along the paths, such that the limited bandwidth is fully utilized to deliver streams of the highest possible quality to the mobile recipients. We also carefully tailor a buffering mechanism on each surrogate to cooperate with optimal stream distribution. We have implemented vSkyConf based on Amazon EC2 and verified the excellent performance of our design, as compared to the widely adopted unicast solutions.Comment: 10 page

ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we describe a novel deep network architecture for computing compact temporal content hashes (TCHs) from audio-visual streams with durations of minutes or hours. Our TCHs are sensitive to accidental or malicious content modification (tampering) but invariant to the codec used to encode the video. This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Cost-effective low-delay cloud video conferencing

The cloud computing paradigm has been advocated in recent video conferencing system design, which exploits the rich on-demand resources spanning multiple geographic regions of a distributed cloud, for better conferencing experience. A typical architectural design in cloud environment is to create video conferencing agents, i.e., virtual machines, in each cloud site, assign users to the agents, and enable inter-user communication through the agents. Given the diversity of devices and network connectivities of the users, the agents may also transcode the conferencing streams to the best formats and bitrates. In this architecture, two key issues exist on how to effectively assign users to agents and how to identify the best agent to perform a transcoding task, which are nontrivial due to the following: (1) the existing proximity-based assignment may not be optimal in terms of inter-user delay, which fails to consider the whereabouts of the other users in a conferencing session; (2) the agents may have heterogeneous bandwidth and processing availability, such that the best transcoding agents should be carefully identified, for cost minimization while best serving all the users requiring the transcoded streams. To address these challenges, we formulate the user-to-agent assignment and transcoding-agent selection problems, which targets at minimizing the operational cost of the conferencing provider while keeping the conferencing delay low. The optimization problem is combinatorial in nature and difficult to solve. Using Markov approximation framework, we design a decentralized algorithm that provably converges to a bounded neighborhood of the optimal solution. An agent ranking scheme is also proposed to properly initialize our algorithm so as to improve its convergence. The results from a prototype system implementation show that our design in a set of Internet-scale scenarios reduces the operational cost by 77% as compared to a commonly-adopted alternative, while simultaneously yielding lower conferencing delays.published_or_final_versio