In free viewpoint video, a viewer can choose at will any camera angle or the so-called “virtual view” to observe a dynamic 3-D scene, enhancing his/her depth perception. The virtual view is synthesized using texture and depth videos of two anchor camera views via depth-image-based rendering (DIBR). We consider, for the first time, collaborative live streaming of a free viewpoint video, where a group of users may interactively pull and cooperatively share streams of different anchor views. There is a cost to access the anchor views from the live source, a cost to “reconfigure” the peer network due to a change in selected anchors during view switching, and a distortion cost due to the distance of the virtual views to the received anchor views at users. We optimize the anchor views allocated to users so as to minimize the overall streaming cost given by the access cost, reconfiguration cost, and view distortion cost. We first show that, if the reconfiguration cost due to view switching is negligible, the view allocation problem can be optimally and efficiently solved in polynomial time using dynamic programming. For the case of non-negligible reconfiguration cost, the problem becomes NP-hard. We thus present a locally optimal and centralized algorithm inspired by Lloyd's algorithm used in non-uniform scalar quantization. We further propose a distributed algorithm with convergence guarantee, where each peer group independently makes merge-and-split decisions with a well-defined fairness criteria. Simulation results show that our algorithms achieve low streaming cost due to its excellent anchor view allocation.

Multiview video refers to videos of the same dynamic 3-D scene captured simultaneously by multiple closely spaced cameras from different viewpoints. We study interactive streaming of pre-encoded multiview videos, where, at any time, a client can request any one of many captured views for playback. Moreover, the client can periodically freeze the video in time and switch to neighboring views for a compelling look-around visual effect. We consider distributed content servers to support large-scale interactive multiview video service. These servers collaboratively replicate and access video contents. We study two challenges in this setting: what is an efficient coding structure that supports interactive view switching and, given that, what to replicate in each server in order to minimize the cost incurred by interactive temporal and view switches? We first propose a redundant coding structure that facilitates interactive view-switching, trading off storage with transmission rate. Using the coding structure, we next propose a content replication strategy that takes advantage of indirect hit to lower view-switching cost: in the event that the exact requested view is not available locally, the local server can fetch a different but correlated view from the other servers, so that the remote repository only needs to supply the pre-encoded view differential. We formulate the video content replication problem to minimize the switching cost as an integer linear programming (ILP) problem and show that it is NP-hard. We first propose an LP relaxation and rounding algorithm (termed Minimum Eviction) with bounded approximation error. We then study a more scalable solution based on dynamic programming and Lagrangian optimization (DPLO) with little sacrifice in performance. Simulation results show that our replication algorithms achieve substantially lower switching cost compared to other content replication schemes.

In order to provide live streaming over the global Internet, a content provider often deploys an overlay network consisting of distributed proxies placed close to user pools. Streaming of multi-Mbps video over such an overlay is challenging because of bandwidth bottlenecks in paths. To effectively overcome these bottlenecks, we consider employing proxy helpers in the overlay to provide rich path diversity. The helpers do not have any attached users, and hence may forward partial video streams (or not at all) if necessary. In this way, the helpers serve as stepping stones to supply full streams to the servers. The issue is how to involve the helpers in the overlay to achieve low streaming delay meeting a certain high streaming bitrate requirement. To address the issue, we first formulate the problem which captures various delay and bandwidth components, and show that it is NP-hard. We then propose an efficient algorithm called Stepping-Stones (SS) which can be efficiently implemented in a controller. Given the encouraging simulation results, we develop a novel streaming testbed for SS and explore, through sets of Internet experiments, the effectiveness of helpers to achieve high bitrate (multi-Mbps) global live streaming. In our experiments, proxies are deployed with a reasonably wide global footprint. We collect more than a hundred hours of streaming traces with bitrate ranging from 500kbps to a few Mbps. Our experimental data validates that helpers indeed play an important role in achieving high bitrate in today's Internet. Global multi-Mbps streaming is possible due to their multihop and multipath advantages. Our experimental trials and data also provide valuable insights on the design of a global push-based streaming network. There are strong benefits of using proxy helpers to achieve high bitrate and low delay.

Due to unpredictable peer churns (joins, leaves and failures), it is challenging to offer video continuity in peer-to-peer (P2P) live streaming. In this paper, we study a push-based P2P network formed by unreliable nodes (i.e., nodes which may churn at any time). To achieve high stream continuity, the video is encoded into k MDC (Multiple-Description Coded) streams and t FEC (Forward Error Correction) streams. To achieve low delay and reduce error correlation between streams, the k + t streams are pushed to the nodes in parent-disjoint spanning trees. The issue is how to construct these trees minimizing the worst-case node delay. We address the optimization of the spanning trees through problem analysis and algorithmic design. After presenting a model capturing important system parameters and delay components, we formulate the problem and prove that it is NP-hard. We then propose SUN (Streaming with Unreliable Nodes), a simple, adaptive and distributed algorithm which continuously reduces delay through overlay adaptation. Through extensive simulation on real Internet and Internet-like topologies, we show that stream continuity can be achieved with push-based P2P streaming. SUN is effective, achieving low delay and high continuity in the presence of node churns for P2P live streaming.

In this study, we discuss how to provide low-delay peer-to-peer streaming with high video quality by considering mesh design with backup parents. To achieve robustness against peer churns, each peer has a certain number of streaming parents and backup parents. We have designed a distributed algorithm that constructed a low-delay mesh and at the same time achieved a certain stream continuity for the peers. We have conducted extensive simulations to study the performance of our algorithms. The results show that our distributed algorithm achieves a lower source-to-peer delay as compared with a traditional scheme. Our results have shown that peer-to-peer live streaming can be delivered in short delays while providing a high level of quality, despite peer churns and the lack of a centralized planner.

Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to a large group. In this paper, we address the design of an overlay mesh which achieves low source-to-peer delay, accommodates asymmetric and diverse uplink bandwidth, and continuously improves delay based on an existing pool of peers. By considering a streaming mesh as an aggregation of data flows along multiple spanning trees, the peer delay in the mesh is then its longest delay (including both propagation and scheduling delay) among all the trees. Clearly, such delay can be very high if the mesh is not designed well. In this paper, we propose and study a mesh protocol called Fast-Mesh, which optimizes such delay while meeting a certain streaming bandwidth requirement. Fast-Mesh is particularly suitable for a mildly dynamic network consisting of proxies, supernodes, or content distribution servers. We first formulate the minimum delay multiple trees (MDMT) problem and show that it is NP-hard. Then we propose a centralized heuristic based on complete knowledge, which may be used when the network is small or managed, and serves as an optimal benchmark for all the other schemes under comparison. We then propose a simple distributed algorithm, Fast-Mesh, where peers select their parents based on the concept of power in networks given by the ratio of throughput and delay. By maximizing the network power, our algorithm achieves low delay. The algorithm makes continuous improvement on delay until some minimum delay is reached. Simulation and PlanetLab experiments show that our distributed algorithm performs very well in terms of delay and source workload, and substantially outperforms traditional and state-of-the-art approaches.

Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to large group. In this paper, we address the design of overlay which achieves low source-to-peer delay, is robust to user churn, accommodates of asymmetric and diverse uplink bandwidth, and continuously improves based on existing user pool. A natural choice is the use of mesh, where each peer is served by multiple parents. Since the peer delay in a mesh depends on its longest path through its parents, we study how to optimize such delay while meeting a certain streaming rate requirement. We first formulate the minimum delay mesh problem and show that it is NP-hard. Then we propose a centralized heuristic based on complete knowledge which serves as our benchmark and optimal solution for all the other schemes under comparison. Our heuristic makes use of the concept of power in network given by the ratio of throughput and delay. By maximizing the network power, our heuristic achieves very low delay. We then propose a simple distributed algorithm where peers select their parents based on the power concept. The algorithm makes continuous improvement on delay until some minimum delay is reached. Simulation results show that our distributed protocol performs close to the centralized one, and substantially outperforms traditional and state-of-the-art approaches.