Breadth First Search time complexity analysis

The time complexity to go over each adjacent edge of a vertex is, say, O(N), where N is number of adjacent edges. So, for V numbers of vertices the time complexity becomes O(V*N) = O(E), where E is the total number of edges in the graph. Since removing and adding a vertex from/to a queue is O(1), why is it added to the overall time complexity of BFS as O(V+E)?

I hope this is helpful to anybody having trouble understanding computational time complexity for Breadth First Search a.k.a BFS.

Queue graphTraversal.add(firstVertex);

// This while loop will run V times, where V is total number of vertices in graph.
while(graphTraversal.isEmpty == false)

    currentVertex = graphTraversal.getVertex();

    // This while loop will run Eaj times, where Eaj is number of adjacent edges to current vertex.
    while(currentVertex.hasAdjacentVertices)
        graphTraversal.add(adjacentVertex);

    graphTraversal.remove(currentVertex);

Time complexity is as follows:

V * (O(1) + O(Eaj) + O(1))
V + V * Eaj + V
2V + E(total number of edges in graph)
V + E

I have tried to simplify the code and complexity computation but still if you have any questions let me know.

Considering the following Graph we see how the time complexity is O(|V|+|E|) but not O(V*E).

Adjacency List

V     E
v0:{v1,v2} 
v1:{v3}
v2:{v3}
v3:{}

Operating How BFS Works Step by Step

Step1:

Adjacency lists:

V     E
v0: {v1,v2} mark, enqueue v0
v1: {v3}
v2: {v3}
v3: {}

Step2:

Adjacency lists:

V     E
v0: {v1,v2} dequeue v0;mark, enqueue v1,v2
v1: {v3}
v2: {v3}
v3: {}

Step3:

Adjacency lists:

V     E
v0: {v1,v2}
v1: {v3} dequeue v1; mark,enqueue v3
v2: {v3}
v3: {}

Step4:

Adjacency lists:

V     E
v0: {v1,v2}
v1: {v3}
v2: {v3} dequeue v2, check its adjacency list (v3 already marked)
v3: {}

Step5:

Adjacency lists:

V     E
v0: {v1,v2}
v1: {v3}
v2: {v3}
v3: {} dequeue v3; check its adjacency list

Step6:

Adjacency lists:

V     E
v0: {v1,v2} |E0|=2
v1: {v3}    |E1|=1
v2: {v3}    |E2|=1
v3: {}      |E3|=0

Total number of steps:

|V| + |E0| + |E1| + |E2| +|E3| == |V|+|E|
 4  +  2   +  1   +   1  + 0   ==  4 + 4
                           8   ==  8

Assume an adjacency list representation, V is the number of vertices, E the number of edges.

Each vertex is enqueued and dequeued at most once.

Scanning for all adjacent vertices takes O(|E|) time, since sum of lengths of adjacency lists is |E|.

Hence The Time Complexity of BFS Gives a O(|V|+|E|) time complexity.