QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach
This addresses the challenge of balancing viewer QoE and resource costs for streaming providers, but it is incremental as it applies existing machine learning methods to a specific domain problem.
The paper tackles the problem of optimizing resource allocation for crowdsourced live streaming by predicting viewer numbers near geo-distributed cloud sites using a machine learning model, and formulates an optimization to maximize viewer quality of experience (QoE) and minimize costs, with predictions shown to be close to actual values.
Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming 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. Moreover, allocating the exact needed resources beforehand avoids over-provisioning, which may lead to significant costs by the service providers. In the contrary, under-provisioning might cause significant delays to the viewers. In this paper, we introduce a prediction driven resource allocation framework, to maximize the QoE of viewers and minimize the resource 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.