A peer-to-peer non-linear media streaming system needs to schedule both on-demand and prefetch requests carefully so as to reduce the server load and ensure good user experience. In this work, we propose, Joserlin, a joint request and service scheduling solution that not only alleviates request contentions (requests compete for limited service capacity), but also schedules the prefetch requests by considering their contributions to potential reduction of server load. In particular, we propose a novel request binning algorithm to prevent self-contention among on-demand requests issued from the same peer. A service and rejection policy is devised to resolve contention among on-demand requests issued from different neighbors. More importantly, Joserlin employs a gain function to prioritize prefetch requests at both requesters and responders, and a prefetch request issuing algorithm to fully utilize available upload bandwidth. Evaluation with traces collected from a popular networked virtual environment shows that Joserlin leads to 20%~60% reduction in server load.

Content discovery is a major source of latency in peer-to-peer (P2P) media streaming systems, especially in the presence of noncontinuous user access, such as random seek in Video-on-Demand (VoD) streaming and teleportation in a Networked Virtual Environment (NVE). After the aforementioned user interactions, streaming systems often need to initiate the content discovery process to identify where to retrieve the requested media objects. Short content lookup latency is demanded to ensure smooth user experience. Existing content discovery systems based on either a Distributed Hash Table (DHT) or gossip mechanism cannot cope with noncontinuous access efficiently due to their long lookup latency. In this work, we propose an access-pattern-driven distributed caching middleware named APRICOD, which caters for fast and scalable content discovery in peer-to-peer media streaming systems, especially when user interactions are present. APRICOD exploits correlations among media objects accessed by users, and adapts to shift in the user access pattern automatically. We first present a general APRICOD design that can be used with any existing content discovery system. We then present an implementation of APRICOD on top of Pastry, which we use to evaluate APRICOD. Our evaluation in a 1024-node system, using a Second Life trace with 5,735 users and a VoD trace with 54 users, shows that APRICOD can effectively resolve all continuous access queries with a single hop deterministically with node failure as an exception, and resolve noncontinuous access queries with a single hop with high probability.

User interactions such as seeks and pauses are widely supported by existing Peer-to-Peer Video-on-Demand (P2P VoD) streaming systems. Their effect on the streaming system, however, has not been well studied. Seeks cause peers to skip part of the video, making them stay in the system for shorter time, and thus contribute less. On the other hand, only part of the video is downloaded due to seeks, reducing peers' demand from the system. It is unclear which factor dominates the effect of seeks on the streaming system. Pauses during playback, on one hand, allow peers to stay longer in the system and upload more content. When interleaved with seeks, however, long pauses may increase peers' demand unnecessarily as peers may download content that will eventually be skipped by subsequent forward seeks. The collective effect of seeks and pauses, together with the known random peer departure, is unintuitive and needs to be addressed properly so as to understand the effect of human factors on the streaming system performance. In this article, we develop an analytical model to both qualitatively and quantitatively study the effect of seeks and pauses on mesh-based P2P VoD streaming systems, in particular, the effect on the server cost. Our model can help in understanding how human factors such as seeks and pauses affect the streaming system performance, tuning a P2P VoD system towards better system performance and stability, and providing a framework for capacity planning.

With the proliferation of interactive media, non-linear and non-continuous media accesses have become a norm. Examples of such interactive media include video-on-demand, massive multiplayer virtual environments, massively multiplayer online game, zoomable video, Google Earth, and free-viewpoint video. To enable scalable delivery of these media contents to potentially millions of users, P2P architecture is often adopted. Non-linear and non-continuous user access pattern, however, pose new challenges on P2P interactive media streaming systems. These challenges include the need of more sophisticated prefetching scheme, increasing server load, high demand for short content discovery time, and difficulty of bandwidth allocation. We first try to understand the effect of non-linear and non-continuous media access on the streaming system performance, in particular, the server load, through analytical modeling. Then a novel user access pattern driven content discovery mechanism is proposed to address the content discovery challenge during non-continuous access. Works aiming at the rest of challenges are ongoing.

We propose an access pattern-driven distributed caching middleware named APRICOD, which can be built on top of any existing content discovery system. APRICOD caters for fast and scalable content discovery in peer-to-peer media streaming systems, especially when user interactions that leads to non-continuous media access (such as random seek in video and teleportation in virtual environment) are present. APRICOD caches query results based correlations among media objects accessed by users, reducing the content discovery time. Our evaluation using a VoD access trace shows that close to 70% of non-continuous access queries can be resolved with a single hop using APRICOD.