On the merits of SVC-based HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the dominant type of video streaming in Over-The-Top multimedia services. HAS content is temporally segmented and each segment is offered in different video qualities to the client. It enables a video client to dynamically adapt the consumed video quality to match with the capabilities of the network and/or the client's device. As such, the use of HAS allows a service provider to offer video streaming over heterogeneous networks and to heterogeneous devices. Traditionally, the H. 264/AVC video codec is used for encoding the HAS content: for each offered video quality, a separate AVC video file is encoded. Obviously, this leads to a considerable storage redundancy at the video server as each video is available in a multitude of qualities. The recent Scalable Video Codec (SVC) extension of H. 264/AVC allows encoding a video into different quality layers: by dowloading one or more additional layers, the video quality can be improved. While this leads to an immediate reduction of required storage at the video server, the impact of using SVC-based HAS on the network and perceived quality by the user are less obvious. In this article, we characterize the performance of AVC- and SVC-based HAS in terms of perceived video quality, network load and client characteristics, with the goal of identifying advantages and disadvantages of both options

Proxy-based near real-time TV content transmission in mobility over 4G with MPEG-DASH transcoding on the cloud

[EN] This paper presents and evaluates a system that provides TV and radio services in mobility using 4G communications. The system has mainly two blocks, one on the cloud and another on the mobile vehicle. On the cloud, a DVB (Digital Video Broadcasting) receiver obtains the TV/radio signal and prepares the contents to be sent through 4G. Specifically, contents are transcoded and packetized using the DASH (Dynamic Adaptive Streaming over HTTP) standard. Vehicles in mobility use their 4G connectivity to receive the flows transmitted by the cloud. The key element of the system is an on-board proxy that manages the received flows and offers them to the final users in the vehicle. The proxy contains a buffer that helps reduce the number of interruptions caused by hand over effects and lack of coverage. The paper presents a comparison between a live transmission using 4G connecting the clients directly with the cloud server and a near real-time transmission based on an on-board proxy. Results prove that the use of the proxy reduces the number of interruptions considerably and, thus, improves the Quality of Experience of users at the expense of slightly increasing the delay.This work is supported by the Centro para el Desarrollo Tecnologico Industrial (CDTI) from the Government of Spain under the project "Plataforma avanzada de conectividad en movilidad" (CDTI IDI-20150126) and the project "Desarrollo de nueva plataforma de entretenimiento multimedia para entornos nauticos" (CDTI TIC-20170102).Arce Vila, P.; De Fez Lava, I.; Belda Ortega, R.; Guerri Cebollada, JC.; Ferrairó, S. (2019). Proxy-based near real-time TV content transmission in mobility over 4G with MPEG-DASH transcoding on the cloud. Multimedia Tools and Applications. 78(18):26399-26425. https://doi.org/10.1007/s11042-019-07840-6S2639926425781

Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.Comment: to appear in IEEE Trans. Multimedi