Shiqiang Yang

Understanding the Power of Pull-Based Streaming Protocol: Can We Do Better?

Most of the real deployed peer-to-peer streaming systems adopt pull-based streaming protocol. In this paper, we demonstrate that, besides simplicity and robustness, with proper parameter settings, when the server bandwidth is above several times of the raw streaming rate, which is reasonable for practical live streaming system, simple pull-based P2P streaming protocol is nearly optimal in terms of peer upload capacity utilization and system throughput even without intelligent scheduling and bandwidth measurement. We also indicate that whether this near optimality can be achieved depends on the parameters in pull-based protocol, server bandwidth and group size. Then we present our mathematical analysis to gain deeper insight in this characteristic of pull-based streaming protocol. On the other hand, the optimality of pull-based protocol comes from a cost -tradeoff between control overhead and delay, that is, the protocol has either large control overhead or large delay. To break the tradeoff, we propose a pull-push hybrid protocol. The basic idea is to consider pull-based protocol as a highly efficient bandwidth-aware multicast routing protocol and push down packets along the trees formed by pull-based protocol. Both simulation and real-world experiment show that this protocol is not only even more effective in throughput than pull-based protocol but also has far lower delay and much smaller overhead. And to achieve near optimality in peer capacity utilization without churn, the server bandwidth needed can be further relaxed. Furthermore, the proposed protocol is fully implemented in our deployed GridMedia system and has the record to support over 220,000 users simultaneously online.

A peer-to-peer network for live media streaming using a push-pull approach

In this paper, we present an unstructured peer-to-peer network called GridMedia for live media streaming employing a push-pull approach. Each node in GridMedia randomly selects its neighbors in the overlay and uses push-pull method to fetch data from the neighbors. The pull mode in the unstructured overlay which is inherently robust can work well with the high churn rate in P2P environment while the push mode can efficiently reduce the accumulated latency observed at user nodes. A practical system based on this framework has been developed. And the performance evaluation of our system which is established on PlanetLab [8] demonstrates that the pull-push method in GridMedia achieves good qualities even in high group change rate. Furthermore, our system was adopted by CCTV to broadcast the Gala Evening for Spring Festival 2005 through the Internet and attracted more than 500,000 users all over the world at that night with the incredibly maximum concurrent users of 15,239.

Large-scale live media streaming over peer-to-peer networks through global internet

We describe the design and implementation of unstructured peer-to-peer networks for large-scale live media streaming through global Internet in this paper. In so called GridMedia system, we adopt gossip-based protocol to organize end nodes into an application layer overlay. Each node in GridMedia independently selects its neighbors and utilizes a novel and efficient push-pull streaming mechanism to fetch data from neighbors with low latency and little redundancy. The traditional pull mode in the unstructured overlay has inherent robustness to high churn rate which is common in peer-to-peer environment while the push mode could efficiently diminish the accumulated latency observed at end users. A practical system based on this architecture has been developed, and we evaluate its performance on PlanetLab [10] in various rigorous conditions. All the results demonstrate that the proposed push-pull method in GridMedia achieves good performance even with high group change rate and very low upload bandwidth limitation. Furthermore, this system was provided for CCTV (the largest TV station in China) to live broadcast the Gala Evening of Spring Festival 2005 over global Internet at the bit rate of 300 Kbps. It is evidenced that more than 500,000 users were attracted all over the world with the peak concurrent online users of 15,239 during the night.

Who should share what?: item-level social influence prediction for users and posts ranking

People and information are two core dimensions in a social network. People sharing information (such as blogs, news, albums, etc.) is the basic behavior. In this paper, we focus on predicting item-level social influence to answer the question Who should share What, which can be extended into two information retrieval scenarios: (1) Users ranking: given an item, who should share it so that its diffusion range can be maximized in a social network; (2) Web posts ranking: given a user, what should she share to maximize her influence among her friends. We formulate the social influence prediction problem as the estimation of a user-post matrix, in which each entry represents the strength of influence of a user given a web post. We propose a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF-NMF problem. Intensive experiments are conducted and demonstrate the advantages and characteristics of the proposed method.

Propagation-based social-aware replication for social video contents

Online social network has reshaped the way how video contents are generated, distributed and consumed on todays Internet. Given the massive number of videos generated and shared in online social networks, it has been popular for users to directly access video contents in their preferred social network services. It is intriguing to study the service provision of social video contents for global users with satisfactory quality-of-experience. In this paper, we conduct large-scale measurement of a real-world online social network system to study the propagation of the social video contents. We have summarized important characteristics from the video propagation patterns, including social locality, geographical locality and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware replication framework using a hybrid edge-cloud and peer-assisted architecture, namely PSAR, to serve the social video contents. Our replication strategies in PSAR are based on the design of three propagation-based replication indices, including a geographic influence index and a content propagation index to guide how the edge-cloud servers backup the videos, and a social influence index to guide how peers cache the videos for their friends. By incorporating these replication indices into our system design, PSAR has significantly improved the replication performance and the video service quality. Our trace-driven experiments further demonstrate the effectiveness and superiority of PSAR, which improves the local download ratio in the edge-cloud replication by 30%, and the local cache hit ratio in the peer-assisted replication by 40%, against traditional approaches.

Optimizing the Throughput of Data-Driven Peer-to-Peer Streaming

During recent years, the Internet has witnessed a rapid growth in deployment of data-driven (or swarming based) peer-to-peer (P2P) media streaming. In these applications, each node independently selects some other nodes as its neighbors (i.e. gossip-style overlay construction), and exchanges streaming data with the neighbors (i.e. data scheduling). To improve the performance of such protocol, many existing works focus on the gossip-style overlay construction issue. However, few of them concentrate on optimizing the streaming data scheduling to maximize the throughput of a constructed overlay. In this paper, we analytically study the scheduling problem in data-driven streaming system and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to optimize the system throughput. Furthermore, we introduce layered video coding into data-driven protocol and extend our algorithm to deal with the end-host heterogeneity. The results of simulation with the real world traces indicate that our distributed algorithm significantly outperforms conventional ad hoc scheduling strategies especially in stringent buffer and bandwidth constraints.

MMC03-4: On the Optimal Scheduling for Media Streaming in Data-driven Overlay Networks

The Internet has witnessed a rapid growth in deployment of data-driven overlay network (DON) based streaming applications during recent years. In these applications, each node independently selects some other nodes as its neighbors (i.e. overlay construction), and exchanges streaming data with these neighbors (i.e. data scheduling). This scheme improves the robustness of the system. However, most of the work in the literature focused on the construction problem, and very few addressed its scheduling problem which is also very important for the overall performance. In this paper, we analytically study the scheduling problem in DON and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to maximize the system throughput. Experimental results indicate that our algorithms outperform other schemes and the throughput gain is up to 80%.

An HMM-based framework for video semantic analysis

Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis.

Key-frame extraction and shot retrieval using nearest feature line (NFL)

Query by key frame or video example is a convenient and often effective way to search in video database. This paper proposes a new approach to support such searches. The main contribution of the proposed approach is the consideration of both feature extraction and distance computation as a whole process. With a video shot represented by key-frames corresponding to feature points in a feature space, a new metric is defined to measure the distance between a query image and a shot based on the concept of Nearest Feature Line (NFL). We propose to use the “breakpoints” of feature trajectory of a video shot as the key frames and use the lines passing through these points to represent the shot. When combined with the NFL method, it helps to achieve a better performance, as evidenced by experiments.

Social recommendation across multiple relational domains

Social networks enable users to create different types of personal items. In dealing with serious information overload, the major problems of social recommendation are sparsity and cold start. In existing approaches, relational and heterogeneous domains can not be effectively utilized for social recommendation, which brings a challenge to model users and multiple types of items together on social networks. In this paper, we consider how to represent social networks with multiple relational domains and alleviate the major problems in an individual domain by transferring knowledge from other domains. We propose a novel Hybrid Random Walk (HRW), which can integrate multiple heterogeneous domains including directed/undirected links, signed/unsigned links and within-domain/cross-domain links into a star-structured hybrid graph with user graph at the center. We perform random walk until convergence and use the steady state distribution for recommendation. We conduct experiments on a real social network dataset and show that our method can significantly outperform existing social recommendation approaches.