A Real-Time Adaptive Algorithm for Video Streaming over Multiple Wireless Access Networks android project | A Real-Time Adaptive Algorithm for Video Streaming over Multiple Wireless Access Networks free download
A Real-Time Adaptive Algorithm for Video Streaming over Multiple Wireless Access Networks
ABSTRACT:
Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets, are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve video streaming quality needs investigation. In order to maintain high video streaming quality while reducing the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of service (QoS) requirements for video traffic, such as the startup latency, playback fluency, average playback quality, playback smoothness and wireless service cost. To solve the MDP in real time, we propose an adaptive, best-action search algorithm to obtain a sub-optimal solution. To evaluate the performance of the proposed adaptation algorithm, we implemented a testbed using the Android mobile phone and the Scalable Video Coding (SVC) codec. Experiment results demonstrate the feasibility and effectiveness of the proposed adaptation algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art adaptation algorithms
EXISTING SYSTEM:
Video streaming is gaining popularity among mobile users recently. Considering that the mobile devices have limited computational capacity and energy supply, and the wireless channels are highly dynamic, it is very challenging to provide high quality video streaming services for mobile users consistently. It is a promising trend to use multiple wireless network interfaces with different wireless communication techniques for mobile devices. Meanwhile, as video data are transmitted over HTTP protocols, the video streaming service can be deployed on any web server. However, the video quality version can only be manually selected by users and such decision can be error-prone.
DISADVANTAGES OF EXISTING SYSTEM:
] The smart phones only have limited storage space, it is impractical to maintain a very large buffer size.
] The buffered unwatched video may be wasted if the user turns off the video player or switches to other videos.
] Download typically does not support transmitting video data over multiple links.
PROPOSED SYSTEM:
In this paper we proposed dynamic adaptive streaming over HTTP has been proposed. In a DASH system, multiple copies of pre-compressed videos with different resolution and quality are stored in segments. We formulate the multi-link video streaming process as a reinforcement learning task. For each streaming step, we define a state to describe the current situation, including the index of the requested segment, the current available bandwidth and other system parameters. A finitestate Markov Decision Process (MDP) can be modeled for this reinforcement learning task. The reward function is carefully designed to consider the video QoS requirements, such as the interruption rate, average playback quality, and playback smoothness, as well as the service costs
ADVANTAGES OF PROPOSED SYSTEM:
ü Smooth and high quality video streaming.
ü Avoid playback interruption and achieve better smoothness and quality.
MODULES:
1. Bandwidth Estimation
2. Real-time Search Algorithm
3. Adaptive Search Depth
4. Space Complexity
MODULES DESCRIPTION:
Bandwidth Estimation:
Rapid network load changes and short-term outages are difficult to predict, and the resultant available bandwidth for a session becomes a time-varying random process. Thus, instead of using a homogeneous Markov chain to estimate the available bandwidth, in our work, a heterogeneous and time-varying Markov model is used to estimate the future bandwidth. The bandwidth of each link will be divided into several regions. Each region will represent a state of the Markov channel model, and the total number of the states is equal to the number of regions. Assume that there are n states, then an n×n transition matrix P will be used for the Markov channel model. Each element pi j is the transition probability from state I to j. To obtain the transition probability, another n×n matrix C is used to count the number of transitions for each state. Once a segment has been successfully downloaded, the transmission bandwidth can be calculated by dividing the total size of the data transmitted over the total transmission time.
Real-time Search Algorithm:
It is easy to note that the best long-term reward for the current state is determined by all the possible future states. Since dynamic programming considers all the possible future steps to obtain the optimal solution, it results in an extremely long computation time. If only part of the future steps is considered, a sub-optimal solution can be obtained. Based on this idea, we develop a real-time recursive best-action search algorithm, which is shown in Algorithm 1. To meet the requirement of the real-time search, an important issue is to reduce the search duration for each state to an acceptable value. We achieve this goal by setting a small search depth D to invoke the search algorithm. For the current states, all the possible actions A(s) will be enumerated. The recursive reward search algorithm is invoked to obtain the reward of states with action a by enumerating all the possible future states S and their associated actions.
Adaptive Search Depth:
Search depth is an important issue in our work. The search depth can determine how good the search result is, and a larger value of depth will achieve a better result. Meanwhile, with the increment of the search depth, the search time to obtain the action for a segment will be increased exponentially. Therefore, the search depth can be viewed as a trade-off between the video quality and the search time. Based on several preliminary experiment results, when the search depth D is larger than three, it will take more than two seconds to obtain a decision on the test Android smart phone. Thus, the maximum search depth Dmax is set to three. As the perceived video streaming fluency is generally considered as one of the most important QoS for the user, the search depth Dis determined by the current queue length in our work.
Space Complexity:
According to the MDP can be viewed as a decision tree. The current state represents the root of the decision tree, and the future possible actions and states form the node and leaves. Since the recursive search will not try the next action until it reaches the leaves. Thus, our real-time search algorithm is a depth-first algorithm. It is easy to find that the computational complexity of our real-time search algorithm is O(b D ),where b is the total number of branches of the search tree and D is the search depth. If we only search one step, it is a typical greedy algorithm. When the search depth is equal to the total number of video segments Nt, then it is exactly identical to the dynamic programming algorithm. There is no need to store all the states and actions in the stack while searching the tree, so the memory consumption of our recursive search algorithm is not high. Generally, the space complexity of our algorithm is bound by O (bD).
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System : Pentium IV 2.4 GHz.
Ø Hard Disk : 40 GB.
Ø Floppy Drive : 1.44 Mb.
Ø Monitor : 15 VGA Colour.
Ø Mouse : Logitech.
Ø Ram : 512 Mb.
Ø MOBILE : ANDROID
SOFTWARE REQUIREMENTS:
Ø Operating system : Windows XP/7.
Ø Coding Language : Java 1.7
Ø Tool Kit : Android 2.3 ABOVE
Ø IDE : Eclipse
VIDEO OUTPUT
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