On Resource Aware Algorithms in Epidemic Live Streaming

Epidemic-style diffusion schemes have been previously proposed for achieving peer-to-peer live streaming. Their performance trade-offs have been deeply analyzed for homogeneous systems, where all peers have the same upload capacity. However, epidemic schemes designed for heterogeneous systems have not been completely understood yet. In this report we focus on the peer selection process and propose a generic model that encompasses a large class of algorithms. The process is modeled as a combination of two functions, an aware one and an agnostic one. By means of simulations, we analyze the awareness-agnostism trade-offs on the peer selection process and the impact of the source distribution policy in non-homogeneous networks. We highlight that the early diffusion of a given chunk is crucial for its overall diffusion performance, and a fairness trade-off arises between the performance of heterogeneous peers, as a function of the level of awareness

QoE in Pull Based P2P-TV Systems: Overlay Topology Design Tradeoff

Abstract—This paper presents a systematic performance anal-ysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: 1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; 2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; 3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support. I

QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach

In the last decade, empowered by the technological advancements of mobile devices and the revolution of wireless mobile network access, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a stable high-quality playback experience is compulsory to maximize the viewers’ Quality of Experience and the content providers’ profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Additionally, because of the instability of network condition and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally expensive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On demand renting of resources or inadequate resources reservation may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than the required, the extra resources will be wasted. In this thesis, we introduce a prediction-driven resource allocation framework, to maximize the QoE of viewers and minimize the resources allocation cost. First, by exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers’ proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation. Considering the complexity and infeasibility of our offline optimization to respond to the volume of viewing requests in real-time, we further extend our work, by introducing a resources forecasting and reservation framework for geo-distributed cloud sites. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers’ proximity, while creating a tradeoff between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site. The results showed that the predicted number of transcoding resources needed in each cloud site is close to the optimal number of transcoding resources

A Gossip-based optimistic replication for efficient delay-sensitive streaming using an interactive middleware support system

While sharing resources the efficiency is substantially degraded as a result of the scarceness of availability of the requested resources in a multiclient support manner. These resources are often aggravated by many factors like the temporal constraints for availability or node flooding by the requested replicated file chunks. Thus replicated file chunks should be efficiently disseminated in order to enable resource availability on-demand by the mobile users. This work considers a cross layered middleware support system for efficient delay-sensitive streaming by using each device's connectivity and social interactions in a cross layered manner. The collaborative streaming is achieved through the epidemically replicated file chunk policy which uses a transition-based approach of a chained model of an infectious disease with susceptible, infected, recovered and death states. The Gossip-based stateful model enforces the mobile nodes whether to host a file chunk or not or, when no longer a chunk is needed, to purge it. The proposed model is thoroughly evaluated through experimental simulation taking measures for the effective throughput Eff as a function of the packet loss parameter in contrast with the effectiveness of the replication Gossip-based policy.Comment: IEEE Systems Journal 201