Online Learning Adaptation Strategy for DASH Clients

In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations

Performance analysis of AIMD mechanisms over a multi-state Markovian path

WeanalLF the performance of an Additive IncreaseMulasebflgg)Lb Decrease(AIMD)-lTF flowcontrol mechanism. The transmission rate is considered to increase lcrease in timeuntil the receipt of a congestion notification, when the transmission rate is mulfi(Fb.flxwwxbl decreased. AIMD captures the steady state behavior of TCP in the absence of timeouts and in the absence of maximum window size lebflw)wTb. We introduce ageneral fluidmodel based on amulT)b.flfifl Markov chain for the moments at which the congestion is detected. With thismodel we are abl to account for correl -bTL and burstiness in congestion moments. Furthermore, we specifyseveral simpl versions of ourgeneral model and then we identify their parameters fromreal TCP traces

Performance Analysis of AIMD Mechanisms over a Multi-state Markovian Path

We analyze the performance of an Additive Increase Multiplicative Decrease (AIMD)-like flow control mechanism. The transmission rate is considered to increase linearly in time until the receipt of a congestion notification, when the transmission rate is multiplicatively decreased. AIMD captures the steady state behavior of TCP in the absence of timeouts and in the absence of maximum window size limitation. We introduce a general fluid model based on a multi-state Markov chain for the moments at which the congestion is detected. With this model, we are able to account for correlation and burstiness in congestion moments. Furthermore, we specify several simple versions of our general model and then we identify their parameters from real TCP traces