A deep learning based approach for content placement in Edge Computing based on the content popularity.
. Deep Learning Model for Content Aware Caching at MEC Servers (Manuscript under Preparation)
Improving the overall Quality of Experience (QoE) of a client for a given video streaming session is a challenging task in a continuously varying network environment. A dynamic bit-rate adaptation strategy at the client side that can maximize the overall QoE for a session is required irrespective of the bandwidth allocation scheme adopted in-network. Even though there are a plethora of state-of-the-art strategies for adaptive bit-rate streaming, they suffer from a few key shortcomings which may significantly restrict the overall QoE potentially achievable by an end-user. For instance, QoE management using a fixed set of rules may not always guarantee optimal bandwidth utilization, video quality enhancement and accurate buffer estimation, especially in the face of severely varying and often unpredictable bandwidth fluctuations. To handle these issues across a wide range of varying network conditions and QoE parameters, machine learning strategies are being used in recent times. ML approaches train the QoE models based on diverse observations related to network bandwidth, actually received bit-rate, segment size, etc, corresponding to video segments received in the past.
. Anirban Lekharu, K Y Mouli, Arijit Sur and Arnab Sarkar, "Deep learning based prediction model for adaptive video streaming", In 12th InternationalConference on COMmunication Systems NETworkS(COMSNETS 2020), 8 January 2020
. A. Lekharu, S. Kumar, A. Sur, A. Sarkar, A QoE Aware LSTM based Bit-Rate Prediction Model for DASH Videos. 10th International Conference on COMmunication Systems and NETworkS (COMSNETS 2018), https://ieeexplore.ieee.org/document/8328225
. A. Lekharu, S. Kumar, A.sur, A.Sarkar, "A QoE Aware SVC Based Client-side Video Adaptation Algorithm for Cellular Networks". International Conference on Distributed Computing and Networking (ICDCN), 2018. Web: https://doi.org/10.1145/3154273.3154312