The simulation and evaluation of diverse 360° video streaming systems present inherent challenges due to the varied design objectives, streaming strategies, and evaluation metrics. This poster introduces E3PO, a versatile and extensible simulation and evaluation platform for 360° video streaming systems. E3PO excels in simulating all proposed variations of 360° video streaming methods, while also generating the precise view that users experience. We have implemented E3PO and developed a variety of examples that simulate different streaming approaches. The promising results from our evaluation of these simulated scenarios demonstrate E3PO's substantial potential to assist researchers in testing new designs, fine-tuning parameters, and comparing performance with their counterparts.

Tile-based approach is widely adopted in adaptive 360\textdegree~video streaming systems. Existing QoE-driven streaming approaches usually obtain the tile selection and adjust the bitrate based on the viewport prediction with a fixed tiling, which fail to consider the unstable prediction performance. However, varying the tiling of the video can produce different number of tiles with different sizes, and thus can have distinct impacts on error tolerance for viewport prediction and on decoding complexity for resource-constrained mobile client. In this work, we introduce adaptive tiling into the conventional bitrate adaptation for mobile 360degree~video streaming. We first analyze the impacts of tilings on tile selection and decoding time, which verify the benefit of tiling adaptation in various practical aspects. We then formulate the QoE optimization problem for adaptive tiling and bitrate streaming and discuss the design details of our adaptation algorithm, which can adapt to the performance of viewport prediction and the decoding capabilities of mobile clients in addition to the conventional influencing factors. Finally, the superiority of our proposed approach compared with the state-of-the-art methods is evaluated through extensive trace-driven simulations.

Emerging multimedia applications like VR, AR, etc., exhibit unique transmission features, such as block-based transmission, dynamic prioritization for different contents, and deadline-aware delivery, which should be carefully managed but fail to be considered in the design of existing transmission control algorithms. In this work, we propose a delay-sensitive congestion control algorithm with a hybrid of coarse-grained and fine-grained control to improve the QoE scores. The coarse-grained control scheme maintains a low queuing delay and avoids missing the deadline in the steady state. The fine-grained control scheme rapidly reacts to the network dynamics based on our bandwidth estimation model. For the block scheduling, we heuristically model the realistic priority of each block by examining the trade-off among the remaining time, the remaining size, and the priority score of each block. Extensive experiments are conducted to evaluate the performance of our solution, which show that our solution significantly outperforms other baseline algorithms.