Analysis of adaptive video streaming for users with demand heterogeneity

The revolution of smart phones and other electronic gadgets have increased the attention given to video streaming phenomena. The demand of video quality has also augmented as the number of users keeps on growing. This has toughened the distribution of bandwidth. It is becoming more challenging for service providers to cope with varied demand of video quality. In real life, users change frequently their demand of video quality. To mitigate these problems, an adaptive streaming approach was proposed to solve the problem related to users’ heterogeneous demands. A linear programing approach based on demand and supply was used to analyse and sustain user with low bandwidth. Enhancement Fractional Participative Scheme (EFPS) based on bandwidth contribution was explored. Using JSVM9.14 software, the simulated result shows that the optimized algorithms and enhancement structure improve the performance

Refined adaptive video quality demands over heterogeneous networks

User demands of video quality are increasing rapidly. Videos streaming providers have been trying hard to fulfill users’ requests of best video qualities over heterogeneous networks. However it is extremely challenging to meet adequate bandwidth which guaranty users’ pleasance. With current portable devices, users change dynamically their positions and demands, thus anticipating the distribution methods of bandwidths and channel capacities. Two modules are used to perform the quality adaptation: the First Adaptive Video Quality (FAVQ) and the Refined Adaptive Video Quality (RAVQ). Both modules form the algorithms that match the layers with resources available at the peer. On one hand, to determine the highest potential layer which user can retrieve and play, the FAVQ is used and perform at session start. On the other hand, due to the changes in environmental network, the RAVQ is performed regularly to adjust the layer accordingly. JSVM 9.14 was used for performance evaluation of the developed schemes. This study manage to formulate the problem as bandwidth reduction and still achieve short delay and maintain acceptable video streaming quality base on scalable video coding (SVC)

EMB: Efficient Multimedia Broadcast in Multi-tier Mobile Networks

Multimedia broadcast and multicast services (MBMS) in mobile networks has been widely addressed, however an investigation of such a technology in emerging, multi-tier, scenarios is still lacking. Notably, user clustering and resource allocation are extremely challenging in multi-tier networks, and imperative to maximize system capacity and improve quality of user-experience (QoE) in MBMS. Thus, in this paper we propose a clustering and resource allocation approach, named EMB, which specifically addresses heterogeneous networks and accounts for the fact that multimedia content is adaptively encoded into scalable layers depending on the QoE requirements and channel conditions of the heterogeneous users. Importantly, we prove that our clustering algorithm yields Pareto efficient broadcasting areas, multimedia encoding parameters, and re- source allocation, in a way that is also fair to the users. Fur- thermore, numerical results obtained under realistic conditions and using real-world video content, show that the proposed EMB results in lower churn count (i.e., higher number of served users), higher throughput, and increased QoE, while using fewer network resources