Yipeng Zhou

A unifying model and analysis of P2P VoD replication and scheduling

We consider a peer-to-peer (P2P)-assisted video-on-demand (VoD) system where each peer can store a relatively small number of movies to offload the server when these movies are requested. User requests are stochastic based on some movie popularity distribution. The problem is how to replicate (or place) content at peer storage to minimize the server load. Several variations of this replication problem have been studied recently with somewhat different conclusions. In this paper, we first point out and explain that the main difference between these studies is in how they model the scheduling of peers to serve user requests, and show that these different scheduling assumptions will lead to different “optimal” replication strategies. We then propose a unifying request scheduling model, parameterized by the maximum number of peers that can be used to serve a single request. This scheduling is called Fair Sharing with Bounded Degree (FSBD). Based on this unifying model, we can compare the different replication strategies for different degree bounds and see how and why different replication strategies are favored depending on the degree. We also propose a simple (primarily) distributed replication algorithm and show that this algorithm is able to adapt itself to work well for different degrees in scheduling.

Video Popularity Dynamics and Its Implication for Replication

Popular online video-on-demand (VoD) services all maintain a large catalog of videos for their users to access. The knowledge of video popularity is very important for system operation , such as video caching on content distribution network (CDN) servers. The video popularity distribution at a given time is quite well understood. We study how the video popularity changes with time, for different types of videos, and apply the results to design video caching strategies. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a videos popularity typically pass by quickly and the probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. All these facts will affect how video popularity changes, hence also affect the optimal video caching strategy. Based on the observation from our measurement study, we propose a mixed replication strategy (of LFU and FIFO) that can handle different kinds of videos. Offline strategy assuming tomorrows video popularity is known in advance is used as a performance benchmark. Through trace-driven simulation, we show that the caching performance achieved by the mixed strategy is very close to the performance achieved by the offline strategy.

Analytical QoE Models for Bit-Rate Switching in Dynamic Adaptive Streaming Systems

Video streaming service in wireless networks is increasingly using dynamic selection of video bit-rates to provide a high quality of user experience (QoE). The bit-rate switching mechanism, performed at client side, plays a key role in determining QoE metrics. In this paper, we present the first analytical framework to compute starvation probability of playout buffer, continuous playback time and mean video quality, given the bit-rate switching logics. Wireless channel is modeled as a continuous time Markov process, and playout buffer is modeled as a fluid queue with Markov modulated fluid arrival. We construct a set of ordinary differential equations (ODEs) to characterize the dynamics of starvation probability and expected continuous playback time with regard to buffer length, and simple models to analyze mean bit-rate for different bit-rate switching algorithms. Our framework is very general in that by adding appropriate parameters, it can be utilized to predict the QoE metrics of dynamic adaptive streaming with a variety of features: i) buffer-aware bit-rate switching ii) (im)patience of the user, and iii) receiver-side flow control.

A lifetime model of online video popularity

Popular online Video-on-Demand (VoD) services all maintain a large catalog of videos for their users to access. The number of servers assigned to serve each video is directly related to the relative popularity of the video. The distribution of popularity at a given time is quite well understood. We study how the video popularity changes over its lifetime, for different types of videos. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a video typically pass by quickly and probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. We then discuss the implication of our findings for video replication and recommendation.

Analysis and Detection of Fake Views in Online Video Services

Online video-on-demand(VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this article. We develop some algorithms and show that they are quite effective for this problem.

Fake View Analytics in Online Video Services

Online video-on-demand (VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect the fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this paper. We develop some algorithms and show their effectiveness for this problem.

Modeling Buffer Starvations of Video Streaming in Cellular Networks with Large-Scale Measurement of User Behavior

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station (BS). Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the “least” information from content providers.

Towards Cost Minimization With Renewable Energy Sharing in Cooperative Residential Communities

The recent increasing evolution of renewable energy technologies makes it possible for common residents to afford the cost of installing renewable energy devices (REDs) and energy storage systems (ESSs) in their own houses. With the prevalence of REDs and ESSs, it is a beneficial and also promising idea for residents in a community to share extra energy with others, especially, when they have different electricity usage patterns. However, considering the unpredictable energy usage patterns, radically intermittent characteristics of renewable energy generation, and dynamic electricity price, it would be difficult for residents in a community to intelligently share their energy with others and thus minimize the overall cost of the whole community. In this paper, we design an online algorithm, which can tackle cost-aware energy sharing among residents in a cooperative community. We formulate the problem as a stochastic constrained problem and the objective is to minimize the time-average cost in the whole community, which includes the cost of purchasing electricity from the main grid, and the cost of charging and discharging ESSs. By exploiting the dynamics of electricity price, we can determine the charging and discharging behaviors of ESSs. We explore our method based on the Lyapunov optimization theory, which does not need any future statistics and possesses low computational complexity. Through theoretical analysis of our algorithm, we can conclude that our strategy can approximate the optimality with provable bounds. Meanwhile, we design a revenue division algorithm based on the Nash bargaining theory to fairly share the revenue among residents. We also conduct extensive trace-driven simulations and results show that our algorithm can obtain nearly 12% of cost reduction for the community when compared with noncooperative algorithms, and ensure the fairness among residents in the meanwhile.

Social Group Based Video Recommendation Addressing the Cold-Start Problem

Video recommendation has become an essential part of online video services. Cold start, a problem relatively common in the practical online video recommendation service, occurs when the user who needs video recommendation has no viewing history Cold start consists of the new-user problem and the new-item problem. In this paper, we discuss the new-user one. A promising approach to resolve this problem is to capitalize on information in online social networks OSNs: Videos viewed by a users friends may be good candidates for recommendation. However, in practice, this information is also quite limited, either because of insufficient friends or lack of abundant viewing history of friends. In this work, we utilize social groups with richer information to recommend videos. It is common that users may be affiliated with multiple groups in OSNs. Through members within the same group, we can reach a considerably larger set of users, hence more candidate videos for recommendation. In this paper, by collaborating with Tencent Video, we propose a social-group-based algorithm to produce personalized video recommendations by ranking candidate videos from the groups a user is affiliated with. This algorithm was implemented and tested in the Tencent Video service system. Compared with two state-of-the-art methods, the proposed algorithm not only improves the click-through rate, but also recommends more diverse videos.

Turbocharged Video Distribution via P2P

There are two types of P2P systems satisfying two different user demands: 1) file downloading and 2) video-on-demand (VoD) streaming. An example of file downloading is the original BitTorrent, and examples for VoD streaming include various commercial P2P-based VoD streaming systems such as that offered by PPLive. We have a hypothesis - by combining a type: 1) system and 2) system as a single P2P system, both the file downloading users and the streaming users of the same video will benefit in performance. The reasoning is that at any moment, only a subset of the file downloading peers can provide good service to VoD streaming peers and the VoD streaming peers are only good at providing service to a different subset of the file downloading peers. The former subset is the set of peers close to completing the downloading of the video file; whereas the latter subset is the set of peers starting to download a video. In this paper, we propose a novel design for a mesh-based video distribution system without depending on video replication on streaming peers. We produce simple back-of-the-envelop analysis to show its effectiveness. Then, we further validate our design and compare it with other designs through simulation and experiments in practical networking environment by implementing a prototype.