Zhijie Shen

Peer-to-Peer Media Streaming: Insights and New Developments

Internet media content delivery started to emerge roughly a decade ago, and it has subsequently had a major impact on network traffic and usage. Although traditional client-server systems were used initially for delivering media content, researchers and practitioners soon realized that peer-to-peer (P2P) systems, due to their self-scaling properties, had the potential to improve scalability compared with traditional client-server architectures. Consequently, various P2P media streaming systems have been deployed successfully, and corresponding theoretical investigations have been performed on such systems. The rapid developments in this field raise the need for up-to-date literature surveys to summarize them. In recent years, numerous technological discoveries have been achieved. The focus of this report is to survey and discuss these new findings, which include new technological developments, as well as new understandings of these developments and of the existing P2P streaming techniques, through both novel modeling methodologies and measurement-based studies.

Automatic tag generation and ranking for sensor-rich outdoor videos

Video tag annotations have become a useful and powerful feature to facilitate video search in many social media and web applications. The majority of tags assigned to videos are supplied by users - a task which is time consuming and may result in annotations that are subjective and lack precision. A number of studies have utilized content-based extraction techniques to automate tag generation. However, these methods are compute-intensive and challenging to apply across domains. Here, we describe a complementary approach for generating tags based on the geographic properties of videos. With todays sensor-equipped smartphones, the location and orientation of a camera can be continuously acquired in conjunction with the captured video stream. Our novel technique utilizes these sensor meta-data to automatically tag outdoor videos in a two step process. First, we model the viewable scenes of the video as geometric shapes by means of its accompanied sensor data and determine the geographic objects that are visible in the video by querying geo-information databases through the viewable scene descriptions. Subsequently we extract textual information about the visible objects to serve as tags. Second, we define six criteria to score the tag relevance and rank the obtained tags based on these scores. Then we associate the tags with the video and the accurately delimited segments of the video. To evaluate the proposed technique we implemented a prototype tag generator and conducted a user study. The results demonstrate significant benefits of our method in terms of automation and tag utility.

Spatial-Temporal Tag Mining for Automatic Geospatial Video Annotation

Videos are increasingly geotagged and used in practical and powerful GIS applications. However, video search and management operations are typically supported by manual textual annotations, which are subjective and laborious. Therefore, research has been conducted to automate or semi-automate this process. Since a diverse vocabulary for video annotations is of paramount importance towards good search results, this article proposes to leverage crowdsourced data from social multimedia applications that host tags of diverse semantics to build a spatio-temporal tag repository, consequently acting as input to our auto-annotation approach. In particular, to build the tag store, we retrieve the necessary data from several social multimedia applications, mine both the spatial and temporal features of the tags, and then refine and index them accordingly. To better integrate the tag repository, we extend our previous approach by leveraging the temporal characteristics of videos as well. Moreover, we set up additional ranking criteria on the basis of tag similarity, popularity and location bias. Experimental results demonstrate that, by making use of such a tag repository, the generated tags have a wide range of semantics, and the resulting rankings are more consistent with human perception.

Orientation data correction with georeferenced mobile videos

Similar to positioning data, camera orientation information has become a powerful contextual feature utilized by a number of GIS and social media applications. Such auxiliary information facilitates higher-level semantic analysis and management of video assets in such applications, e.g., video summarization and video indexing systems. However, it is problematic that raw sensor data collected from current mobile devices is often not accurate enough for subsequent geospatial analysis. To date, an effective orientation data correction system for mobile video content has been lacking. Here we present a content-based approach that improves the accuracy of noisy orientation sensor measurements generated by mobile devices in conjunction with video acquisition. Our preliminary experimental results demonstrate significant accuracy enhancements which benefit upstream sensor-aided GIS applications to access video content more precisely.

ISP-friendly P2P live streaming: A roadmap to realization

Peer-to-Peer (P2P) applications generate large amounts of Internet network traffic. The wide-reaching connectivity of P2P systems is creating resource inefficiencies for network providers. Recent studies have demonstrated that localizing cross-ISP (Internet service provider) traffic can mitigate this challenge. However, bandwidth sensitivity and display quality requirements complicate the ISP-friendly design for live streaming systems. To this date, although some prior techniques focusing on live streaming systems exist, the correlation between traffic localization and streaming quality guarantee has not been well explored. Additionally, the proposed solutions are often not easy to apply in practice. In our presented work, we demonstrate that the cross-ISP traffic of P2P live streaming systems can be significantly reduced with little impact on the streaming quality. First, we analytically investigate and quantify the tradeoff between traffic localization and streaming quality guarantee, determining the lower bound of the inter-AS (autonomous system) streaming rate below which streaming quality cannot be preserved. Based on the analysis, we further propose a practical ISP-friendly solution, termed IFPS, which requires only minor changes to the peer selection mechanism and can easily be integrated into both new and existing systems. Additionally, the significant opportunity for localizing traffic is underscored by our collected traces from PPLive, which also enabled us to derive realistic parameters to guide our simulations. The experimental results demonstrate that IFPS reduces cross-ISP traffic from 81% up to 98% while keeping streaming quality virtually unaffected.

OSCOR: an orientation sensor data correction system for mobile generated contents

In addition to positioning data, other sensor information -- such as orientation data, have become a useful and powerful contextual feature. Such auxiliary information can facilitate higher-level semantic description inferences in many multimedia applications, e.g., video tagging and video summarization. However, sensor data collected from current mobile devices is often not accurate enough for upstream multimedia analysis. An effective orientation data correction system for mobile multimedia content has been an elusive goal so far. Here we present a system, termed Oscor, which aims to improve the accuracy of noisy orientation sensor measurements generated by mobile devices during image and video recording. We provide a user-friendly camera interface to facilitate the gathering of additional information, which enables the correction process on the server-side. Geographic field-of-view (FOV) visualizations based on the original and corrected sensor data help users understand the corrected contextual information and how the erroneous data possibly may affect further processes.

LAN-awareness: improved P2P live streaming

The popularity of P2P streaming systems has rapidly created extensive, far-reaching Internet traffic. Recent studies have demonstrated that localizing cross-ISP (Internet service provider) traffic can mitigate this challenge. Another trend shows that households own an increasing number of devices, which are sharing a LAN of 2 or more peers. To this date, however, no study has investigated the potential of localizing traffic within LANs. In our presented work, we propose the concept of LAN-awareness and introduce its threefold benefits: 1) reducing Internet streaming traffic, 2) lowering stream server workload, and 3) improving streaming quality. First we conduct a large-scale measurement on PPLive, confirming that a considerable number of peers (up to 21%) are connected to the LANs having 2 or more peers. Recognizing the opportunity of localizing traffic within LANs, we discuss the principles to construct a LAN-aware overlay and propose a heuristic. The results of our trace-driven simulations confirm the benefits outlined above.

SRV-TaGS: An Automatic TAGging and Search System for Sensor-Rich Outdoor Videos

Tagging facilitates video search in many social media and web applications. While manual tagging is time consuming, subjective and sometimes inaccurate, auto-tagging facilitated by content-based techniques is compute-intensive and challenging to apply across domains. We have developed a complementary system, named SRV-TAGS, to automatically generate tags for outdoor videos based on their geographic properties, to index the videos based on their generated tags and to provide textual search services. The system works with our geo-referenced video management web portal, enabling users to manage, search and watch videos.

Reducing cross-group traffic with a cooperative streaming architecture

Cooperative approaches, such as P2P networks, have demonstrated their effectiveness in video delivery. However, with underlay structure considered, it is still possible to further improve traffic efficiency. In this paper, we discuss the problem of localizing the traffic traversal across peer groups, which are partitioned according to underlay characteristics. We first provide three concrete examples to demonstrate this common challenge, which we theoretically formulate afterwards. Finally, we propose a ring overlay approach, which performs excellently to solve the problem, while tolerating peer dynamics and supporting peer heterogeneity.